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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Lotfi, A., Maalandish, M., Aghaei, A., Samadian, A., Fathollahi, A., Hosseini, S. H. & Khooban, M. H. (2024). Novel multilevel inverter topology with low switch count. International Journal of Emerging Electric Power Systems, 25(3), 305-319. https://doi.org/10.1515/ijeeps-2022-0309

Lorenzen, K. P., Heremans, E. R. M., Vos, M. D. & Mikkelsen, K. B. (2024). Personalization of Automatic Sleep Scoring: How Best to Adapt Models to Personal Domains in Wearable EEG. IEEE Journal of Biomedical and Health Informatics, 28(10), 5804-5815. https://doi.org/10.1109/JBHI.2024.3409165

Liu, Z., Liang, J., Zhang, S. & Kayacan, E. (2022). ANN-Based vibration control of an aerial refueling hose system with input nonlinearity and prescribed output constraint. Journal of the Franklin Institute, 359(6), 2627-2645. https://doi.org/10.1016/j.jfranklin.2022.01.006

Liu, L., Grombacher, D., Griffiths, M., Vang, M. & Larsen, J. J. (2023). Signal Processing Steady-State Surface NMR Data. IEEE Transactions on Instrumentation and Measurement, 72, Artikel 6502313. https://doi.org/10.1109/TIM.2023.3264033

Liu, B., Wang, Y., Mofid, O., Mobayen, S. & Khooban, M. H. (2024). Barrier Function-Based Backstepping Fractional-Order Sliding Mode Control for Quad-Rotor Unmanned Aerial Vehicle under External Disturbances. IEEE Transactions on Aerospace and Electronic Systems, 60(1), 716-728. https://doi.org/10.1109/TAES.2023.3328801

Liu, H., Yang, Y., Wu, Q., He, B., Liao, Y. & Zhou, P. (2024). FacGNN: Multi-faceted Fairness Enhancement for GNN through Adversarial and Contrastive Learning. I 2024 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/IJCNN60899.2024.10649939

Liu, L., Domenzain Gonzalez, D., Vela, I., Maurya, P. K., Kühl, A. K., Bording, T., Ejlskov, P., Auken, E., Larsen, J. J. & Christiansen, A. V. (2025). An efficient cross-borehole direct current resistivity monitoring instrument. Geophysics, 90(2), D61-D70. https://doi.org/10.1190/geo2023-0518.1

Liu, J. (2020). Mobile Edge Computing in 5G. [Ph.d.-afhandling, Aarhus Universitet].

Liu, S., Lang, X., Wu, J. & Rehman, N. U. (2025). Selective Noise Empirical Mode Decomposition. IEEE Signal Processing Letters, 32, 2823-2827. https://doi.org/10.1109/LSP.2025.3588082

Liu, Q., Zheng, Y., Wu, H., Michalek, L., Ronchini, M., Mow, R. K., Wang, W., Park, H., Ji, X., Yu, Z., Yao, Z. F., Nishio, Y., Zhao, C., Pei, J. & Bao, Z. (2026). Intrinsically stretchable complementary circuits based on direct photo-patternable polymer semiconductors. Nature Electronics. Advance online publication. https://doi.org/10.1038/s41928-026-01599-z

Lindahl Petersen , K., Ladegaard Jensen, K., Back Nielsen, M., Pas, L.-C., Jensen, N.-P., Nielsen, P. R., Bøjer, O. M., Nyholm Jørgensen, R., Laursen, M. S., Teimouri, N. & Hartmann, B. (2021). Analyse af mulige herbicidbesparelser ved brug af erfaringer og data fra RoboWeedMaPS. Datalogisk Institut. https://datalogisk.dk/wp-content/uploads/2021/03/MST_20rapport_version_2_2002092021.pdf

Lin, P.-C., Abbaszadeh, E., Mobayen, S., Rouhani, S. H., Su, C.-L., Haddad-Zarif, M. & Khooban, M. H. (2024). Soft variable structure fractional sliding-mode control for frequency regulation in renewable shipboard microgrids. Ocean Engineering, 296, Artikel 117065. https://doi.org/10.1016/j.oceaneng.2024.117065

Lillelund, C. M., Pedersen, C. F., Harbo, M. & Fibiger Smed, C. (2021). Vejen til genoptræning går gennem data. Medicoteknik, 8(3), 18-19. https://ipaper.ipapercms.dk/TechMedia/Medicoteknik/2021/3/?page=18

Lillelund, C. M., Jensen, H. B. & Pedersen, C. F. (2022). Cloud K-SVD for Image Denoising. SN Computer Science, 3(2), Artikel 151. https://doi.org/10.1007/s42979-022-01042-y

Lillelund, C. M., Pannullo, F., Jakobsen, M. O. & Pedersen, C. F. (2023). Predicting Survival Time of Ball Bearings in the Presence of Censoring. I AAAI Symposium on Survival Prediction: Algorithms, Challenges, and Applications (SPACA) AAAI Press.

Lillelund, C. M., Magris, M. & Pedersen, C. F. (2023). Uncertainty Estimation in Deep Bayesian Survival Models. I IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI’23) IEEE. https://doi.org/10.1109/BHI58575.2023.10313466

Lillelund, C. M., Magris, M. & Pedersen, C. F. (2025). Efficient Training of Probabilistic Neural Networks for Survival Analysis. IEEE Journal of Biomedical and Health Informatics, 29(9), 6157-6166. https://doi.org/10.1109/JBHI.2024.3417369

Lillelund, C. M., Harbo, M. & Pedersen, C. F. (2026). Prognosis of fall risk in home care clients: A noninvasive approach using survival analysis. Journal of Public Health (Germany), 34(4), 905-917. https://doi.org/10.1007/s10389-024-02317-9

Lillelund, C. M., Kalra, S. & Greiner, R. (2025). A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis. PLoS One, 20(11 ), Artikel e0336476. https://doi.org/10.1371/journal.pone.0336476

Liang, W., Amer, A., Mehndiratta, M., Chen, Z., Yao, B. & Kayacan, E. (2025). Adaptive Robust Control Integrated With Gaussian Processes for Quadrotors: Enhanced Accuracy, Fault Tolerance and Anti-Disturbance. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 55(5), 3235-3248. https://doi.org/10.1109/TSMC.2025.3539707

Liang, P., Yang, M., Wu, Z., Zhou, P. Y. & Liao, Y. (2026). DiSCo: Disrupting Semantic Consistency for Transferable Cross-Modal Adversarial Attacks. I J. Lokoc, L. Peška, J. Zahálka, S. Rudinac, U. Sharma, M. Kastner, J. Chen, M.-C. Hu & J. Wu (red.), MultiMedia Modeling - 32nd International Conference on Multimedia Modeling, MMM 2026, Proceedings (s. 16-30). Springer Science+Business Media. https://doi.org/10.1007/978-981-95-6957-1_2

Li, B., Schultz, C. P. L., Melzner, J., Golovina, O. & Teizer, J. (2020). Safe and Lean Location-based Construction Scheduling. I H. Osumi, H. Furuya & K. Tateyama (red.), Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC 2020): From Demonstration to Practical Use - To New Stage of Construction Robot - (s. 1409-1416). International Association for Automation and Robotics in Construction (IAARC). https://doi.org/10.22260/ISARC2020/0195

Li, B., Schultz, C. P. L., Dimyadi, J. & Amor, R. (2021). Defeasible reasoning for automated building code compliance checking. I V. Semenov & R. J. Scherer (red.), ECPPM 2021 – eWork and eBusiness in Architecture, Engineering and Construction: Proceedings of the 13th European Conference on Product & Process Modelling (ECPPM 2021), 15-17 September 2021, Moscow, Russia (s. 229-236). CRC Press. https://doi.org/10.1201/9781003191476

Li, B., Schultz, C., Teizer, J., Golovina, O. & Melzner, J. (2022). Towards a unifying domain model of construction safety, health and well-being: SafeConDM. Advanced Engineering Informatics, 51, Artikel 101487. https://doi.org/10.1016/j.aei.2021.101487

Li, B., Fitzgerald, J. & Schultz, C. (2022). Modelling the impacts of crowds on occupants in the built environment—A static, rule-based approach to human perception and movement. Advanced Engineering Informatics, 51, Artikel 101452. https://doi.org/10.1016/j.aei.2021.101452

Li, N., Iosifidis, A. & Zhang, Q. (2022). Collaborative edge computing for distributed CNN inference acceleration using receptive field-based segmentation. Computer Networks, 214, Artikel 109150. https://doi.org/10.1016/j.comnet.2022.109150

Li, N., Iosifidis, A. & Zhang, Q. (2022). Receptive Field-based Segmentation for Distributed CNN Inference Acceleration in Collaborative Edge Computing. I ICC 2022 - IEEE International Conference on Communications (s. 4281-4286). IEEE. https://doi.org/10.1109/ICC45855.2022.9838547

Li, N., Iosifidis, A. & Zhang, Q. (2022). Distributed Deep Learning Inference Acceleration using Seamless Collaboration in Edge Computing. I ICC 2022 - IEEE International Conference on Communications (s. 3667-3672). IEEE. https://doi.org/10.1109/ICC45855.2022.9839083

Li, N., Iosifidis, A. & Zhang, Q. (2022). Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing. I 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings (s. 982-987). IEEE. https://doi.org/10.1109/GLOBECOM48099.2022.10001067

Li, R., Shreya, S., Ricci, S., Bridarolli, D., Ielmini, D., Farkhani, H. & Moradi, F. (2023). Thermal-Induced Multi-State Memristors for Neuromorphic Engineering. I IEEE International Symposium on Circuits and Systems (ISCAS): Proceedings IEEE. https://doi.org/10.1109/ISCAS46773.2023.10182122

Li, H., Zhang, Q. & Kuldeep, G. (2023). Demo: 3TierView: A Three-tier Privacy-preserving Live Video Surveillance IoT System. I MobiSys '23: Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services (s. 594–595). Association for Computing Machinery. https://doi.org/10.1145/3581791.3597289

Li, N., Iosifidis, A. & Zhang, Q. (2023). Attention-Based Feature Compression for CNN Inference Offloading in Edge Computing. I M. Zorzi, M. Tao & W. Saad (red.), ICC 2023 - IEEE International Conference on Communications (s. 967-972). IEEE. https://doi.org/10.1109/ICC45041.2023.10279110

Li, N., Bennis, M., Iosifidis, A. & Zhang, Q. (2023). Spatiotemporal Attention-based Semantic Compression for Real-time Video Recognition. I IEEE Globecom 2023 (s. 1603-1608). IEEE. https://doi.org/10.1109/GCWkshps58843.2023.10465187

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, Artikel 122779. https://doi.org/10.1016/j.apenergy.2024.122779

Li, B. & Schultz, C. P. L. (2025). Clingo2DSR - A clingo-based software system for declarative spatial reasoning. Spatial Cognition & Computation, 25(1), 69-119. https://doi.org/10.1080/13875868.2024.2324875

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), Artikel 10043. https://doi.org/10.1038/s41598-024-60929-3

Li, H., Ma, L., Shi, H., Hao, Y., Liao, Y., Cheng, L. & Zhou, P. Y. (2025). 3D-GOI: 3D GAN Omni-Inversion for Multifaceted and Multi-object Editing. I A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler & G. Varol (red.), Computer Vision – ECCV 2024 - 18th European Conference, Proceedings (s. 390-406). Springer Science+Business Media. https://doi.org/10.1007/978-3-031-73033-7_22

Li, H., Shi, H., Zhang, W., Wu, W., Liao, Y., Wang, L., Lee, L. H. & Zhou, P. Y. (2025). DreamScene: 3D Gaussian-Based Text-to-3D Scene Generation via Formation Pattern Sampling. I A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler & G. Varol (red.), Computer Vision – ECCV 2024 - 18th European Conference, Proceedings (s. 214-230). Springer Science+Business Media. https://doi.org/10.1007/978-3-031-72904-1_13

Li, N., Iosifidis, A. & Zhang, Q. (2025). Dynamic Semantic Compression for CNN Inference in Multi-Access Edge Computing: A Graph Reinforcement Learning-Based Autoencoder. IEEE Transactions on Wireless Communications, 24(3), 2157-2172. https://doi.org/10.1109/TWC.2024.3518399

Li, N. (2023). Edge AI for Time-critical Internet-of-things. [Ph.d.-afhandling, Aarhus Universitet]. Aarhus University.

Li, J., Li, Z., Liu, Z., Zhou, P. Y., Hong, R., Li, Q. & Hu, H. (2025). Viewport Prediction for Volumetric Video Streaming by Exploring Video Saliency and User Trajectory Information. IEEE Transactions on Circuits and Systems for Video Technology, 35(12), 12816-12829. https://doi.org/10.1109/TCSVT.2025.3577724

Li, B. (2022). Answer Set Programming for Building Information Modelling. [Ph.d.-afhandling, Aarhus Universitet].

Li, J., Zhao, Z., Li, Q., Li, Z., Zhou, P., Liu, Z., Zhou, H. & Li, Z. (2025). VPFormer: Leveraging Transformer with Voxel Integration for Viewport Prediction in Volumetric Video. ACM Transactions on Multimedia Computing, Communications and Applications, 21(6), Artikel 158. https://doi.org/10.1145/3730402

Li, H., Tian, Y., Lan, K., Liao, Y., Wang, L., Hui, P. & Zhou, P. Y. (2025). DreamScene: 3D Gaussian-based End-to-end Text-to-3D Scene Generation. IEEE Transactions on Pattern Analysis and Machine Intelligence. Advance online publication. https://doi.org/10.1109/TPAMI.2025.3620403

Leporowski, B. & Iosifidis, A. (2021). Visualising deep network time-series representations. Neural Computing and Applications, 33(23), 16489-16498. https://doi.org/10.1007/s00521-021-06244-8

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. I A.-L. Andersen, R. Andersen, T. D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (red.), 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 (s. 224-232). Springer. https://doi.org/10.1007/978-3-030-90700-6_25

Leka, S. & Jacobsen, H. S. (2022). The role of Idea management system in Absorptive Capacity and Action-based Process of Radical innovation. I Event Proceedings: LUT Scientific and Expertise Publications LUT Scientific and Expertise Publications.

Leka, S. (2024). The Role of Artificial Intelligence in Idea Management Systems and Innovation Processes: An Integrative Review. I AICCONF '24: Proceedings of the Cognitive Models and Artificial Intelligence Conference (s. 160-164). Association for Computing Machinery. https://doi.org/10.1145/3660853.3660890

Leka, S. & Shah, A. (2025). Advancing Idea Management Systems with Topic Modeling: Radical Innovation Patterns in Student-Driven Industry Cases. I 2025 3rd Cognitive Models and Artificial Intelligence Conference, AICCONF 2025 - Proceedings IEEE. https://doi.org/10.1109/AICCONF64766.2025.11064163

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), Artikel 223001. https://doi.org/10.1088/1361-6463/acbe4c

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