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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Merhi, Y. (2025). Piezoelectric and Crystallinity Enhancements in Poly-L-Lactide Acid (PLLA) for Bioresorbable Biomedical Sensor. [Ph.d.-afhandling, Aarhus Universitet].

Merhi, Y., Goumarre, V., Romanyuk, K., Amarasinghe, Y., Kholkin, A., Klarskov, P. & Agarwala, S. (2025). Advancing green electronics: tunable piezoelectric enhancement in biodegradable poly(l-lactic acid) PLLA films through thermal-strain engineering. Nanoscale Horizons, 10(7), 1414-1427. https://doi.org/10.1039/d5nh00142k

Mendes, J. M., Frasheri, M. & Larsen, P. G. (2025). Digital Twin-Enabled Collision Mitigation for Path Constrained Applications. I 2025 11th International Conference on Automation, Robotics, and Applications (ICARA) (2025 udg., s. 394-398) https://doi.org/10.1109/ICARA64554.2025.10977591

Meldgaard Madsen, L., Asif, M. R., Maurya, P. K., Kühl, A. K., Domenzain, D., Jensen, C., Martin, T., Bastani, M. & Persson, L. (2023). Comparison of tTEM-IP and ERT-IP: Cases from Mine Tailing Sites in Sweden. Abstract fra NSG2023 29th European Meeting of Environmental and Engineering Geophysics, Edinburgh , Storbritannien. https://doi.org/10.3997/2214-4609.202320114

Mehndiratta, M., Camci, E. & Kayacan, E. (2021). Can deep models help a robot to tune its controller? A step closer to self-tuning model predictive controllers. Electronics, 10(18), Artikel 2187. https://doi.org/10.3390/electronics10182187

McLachlan, P. J., Khare, S. K., Grombacher, D., Larsen, J. J., A. Christiensen, A. & Luria, J. C. Z. (2022). On The Presence of Correlated Noise in Transient Electromagnetic (Tem) Monitoring Data. 1-5. Abstract fra NSG2022 28th European Meeting of Environmental and Engineering Geophysics, Belgrade , Serbien. https://doi.org/10.3997/2214-4609.202220119

McLachlan, P. J., Khare, S. K., Grombacher, D., Larsen, J. J., Christensen, A. & Zamora Luria, J. C. (2022). On The Presence of Correlated Noise in Transient Electromagnetic (Tem) Monitoring Data. I NSG2022 28th European Meeting of Environmental and Engineering Geophysics (s. 1-5). European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.202220119

Mazur-Milecka, M., Kowalczyk, N., Jaguszewska, K., Zamkowska, D., Wójcik, D., Preis, K., Skov, H., Wagner, S., Sandager, P., Sobotka, M. & Rumiński, J. (2023). Preeclampsia Risk Prediction Using Machine Learning Methods Trained on Synthetic Data. I P. Strumiłło, A. Klepaczko, M. Strzelecki & D. Bociąga (red.), The Latest Developments and Challenges in Biomedical Engineering: Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical Engineering, Lodz, Poland, September 27–29, 2023 (s. 267-281). Springer. https://doi.org/10.1007/978-3-031-38430-1_21

Masoudian, A., Jakobsen, U. & Khooban, M. H. (2025). Emulation of Variational Quantum Circuits on Embedded Systems for Real-Time Quantum Machine Learning Applications. Designs, 9(4), Artikel 87. https://doi.org/10.3390/designs9040087

Marinoudi, V., Lampridi, M., Kateris, D., Pearson, S., Sørensen, C. G. & Bochtis, D. (2021). The future of agricultural jobs in view of robotization. Sustainability (Switzerland), 13(21), Artikel 12109. https://doi.org/10.3390/su132112109

Marinoudi, V., Benos, L., Camacho Villa, C., Lampridi, M., Kateris, D., Berruto, R., Pearson, S., Sørensen, C. G. & Bochtis, D. (2024). Adapting to the Agricultural Labor Market Shaped by Robotization. Sustainability, 16(16), Artikel 7061. https://doi.org/10.3390/su16167061

Marinoudi, V., Benos, L., Villa, C. C., Kateris, D., Berruto, R., Pearson, S., Sørensen, C. G. & Bochtis, D. (2024). Large language models impact on agricultural workforce dynamics: Opportunity or risk? Smart Agricultural Technology, 9, Artikel 100677. https://doi.org/10.1016/j.atech.2024.100677

Marcano, N. J. H., Diez, L., Calvo, R. A. & Jacobsen, R. H. (2021). On the Queuing Delay of Time-Varying Channels in Low Earth Orbit Satellite Constellations. IEEE Access, 9, 87378-87390. Artikel 9454162. https://doi.org/10.1109/ACCESS.2021.3089005

Marcano, N. J. H., Diez, L., Aguero, R. & Jacobsen, R. H. (2022). Finite Buffer Queuing Delay Performance in the Low Earth Orbit Land Mobile Satellite Channel. I 2022 IEEE Wireless Communications and Networking Conference, WCNC 2022 (s. 132-137). IEEE. https://doi.org/10.1109/WCNC51071.2022.9771859

Marandi, S. A., Sehat, H., Lucani Rötter, D. E., Mousavifar, S. & Jacobsen, R. H. (2021). Network Coding-based Data Storage and Retrieval for Kademlia. I 2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings Artikel 9448845 IEEE. https://doi.org/10.1109/VTC2021-Spring51267.2021.9448845

Marandi, A., Alves, P. G. M. R., Aranha, D. F. & Jacobsen, R. H. (2024). Lattice-based Homomorphic Encryption for Privacy-Preserving Smart Meter Data Analytics. The Computer Journal, 67(5), 1687-1698. https://doi.org/10.1093/comjnl/bxad093

Mantysalo, M., Marrocco, G., Agarwale, S., Grau, G. & Gao, W. (2025). Guest Editorial: Special Issue of Selected Extended Papers From the IEEE International Conference on Flexible, Printable Sensors and Systems (FLEPS) 2024. IEEE Journal on Flexible Electronics, 4(1), 2-3. https://doi.org/10.1109/JFLEX.2025.3556389

Mann, H. M. R., Iosifidis, A., Jepsen, J. U., Welker, J. M., Loonen, M. J. J. E. & Høye, T. T. (2022). Automatic flower detection and phenology monitoring using time-lapse cameras and deep learning. Remote Sensing in Ecology and Conservation, 8(6), 765-777. https://doi.org/10.1002/rse2.275

Malmqvist, J., Lundqvist, U., Rosén, A., Edström, K., Gupta, R., Leong, H., Cheah, S. M., Bennedsen, J., Hugo, R., Kamp, A., Leifler, O., Gunnarsson, S., Roslöf, J. & Spooner, D. (2022). The CDIO Syllabus 3.0 - An Updated Statement of Goals. I 18th CDIO International Conference Proceedings - Full papers (s. 18-36) https://en.ru.is/media/cdio2022/CDIO_2022_Proceedings.pdf

Malik, J., Kiranyaz, S., Al-Raoush, R. I., Monga, O., Garnier, P., Foufou, S., Bouras, A., Iosifidis, A., Gabbouj, M. & Baveye, P. C. (2022). 3D Quantum Cuts for Automatic Segmentation of Porous Media in Tomography Images. Computers & Geosciences, 159, Artikel 105017. https://doi.org/10.1016/j.cageo.2021.105017

Mahto, K. K., Mahato, B., Chandan, B., Das, D., Das, P., Kumari, S., Vita, V., Pavlatos, C. & Fotis, G. (2024). A Modified Criss-Cross-Based T-Type MLI with Reduced Power Components. Technologies, 12(6), Artikel 90. https://doi.org/10.3390/technologies12060090

Mahmoudi, A., Khezri, R., Bidram, A., Khooban, M., Aki, H., Khalilpour, K., Abdeltawab, H. & Muyeen, S. M. (2023). Guest editorial: Application of cloud energy storage systems in power systems. IET Generation, Transmission and Distribution, 17(8), 1687-1689. https://doi.org/10.1049/gtd2.12770

Maheshwari, R., Loidl, M., Khare, R. & Cools, M. (2025). Are we measuring what matters? A multilayered framework evaluating active mobility infrastructure through PLOS, BLOS and 15 minute city principles. Journal of Cycling and Micromobility Research, 6, Artikel 100089. https://doi.org/10.1016/j.jcmr.2025.100089

Magris, M. & Iosifidis, A. (2021). Approximate Bayes factors for unit root testing. Afhandling præsenteret på International Association for Applied Econometrics Annual Conference 2021, Rotterdam, Holland. https://www.researchgate.net/publication/349492007_Approximate_Bayes_factors_for_unit_root_testing

Magris, M. & Iosifidis, A. (2023). Bayesian learning for neural networks: an algorithmic survey. Artificial Intelligence Review, 56(10), 11773-11823. https://doi.org/10.1007/s10462-023-10443-1

Magris, M. & Iosifidis, A. (2023). Variational Inference for GARCH-family Models. I ICAIF 2023 - 4th ACM International Conference on AI in Finance (s. 541-548). Association for Computing Machinery. https://doi.org/10.1145/3604237.3626863

Magris, M., Shabani, M. & Iosifidis, A. (2024). Manifold Gaussian Variational Bayes on the Precision Matrix. Neural Computation, 36(9), 1744-1798. https://doi.org/10.1162/neco_a_01686

Magris, M., Shabani, M. & Iosifidis, A. (2026). Quasi black-box variational inference for Bayesian learning. Pattern Recognition, 177, Artikel 113219. https://doi.org/10.1016/j.patcog.2026.113219

Madupuri, R. P., Enduri, M. K., Sobin, C. C., Koduru, H. & Bandaru, N. (2026). A hybrid machine learning and centrality framework for key node identification in complex networks. Discover Computing, 29(1), Artikel 8. https://doi.org/10.1007/s10791-025-09900-y

Madsen, E., Tola, D., Hansen, C., Gomes, C. & Larsen, P. G. (2022). AURT: A Tool for Dynamics Calibration of Robot Manipulators. I 2022 IEEE/SICE International Symposium on System Integration, SII 2022 (s. 190-195). IEEE. https://doi.org/10.1109/SII52469.2022.9708769

Madsen, M. L., Ulsig, E. Z., Folsach, S., Godoy, P. H., Stanton, E. J. & Volet, N. (2023). Mid-infrared difference-frequency generation in AlGaAs-on-insulator waveguides. Journal of the Optical Society of America B: Optical Physics, 40(7), 1742-1748. https://doi.org/10.1364/JOSAB.488065

Macedo, H. D. (2021). Digital Twins. 12-15. Afhandling præsenteret på Digitale tvillinger i afløbs- og spildevandsbranchen . http://www.evanet.dk/wp-content/uploads/2021/09/2021_03_EVA_blad.pdf

Macedo, H. D., Thule, C. & Pierce, K. (2021). Proceedings of the 19th International Overture Workshop. https://arxiv.org/abs/2110.09371

Macedo, H. D., Højberg, J., Jensen, J. & Thomsen, U. (2022). Data-Driven Extraneous Water Quantification. Abstract fra 12th Urban Drainage Modeling Conference. https://udm2022.org/2021/11/09/data-driven-extraneous-water-quantification/

Macedo, H. D., Sassanelli, C., Larsen, P. G. & Terzi, S. (2021). Facilitating model-based design of cyber-manufacturing systems. Procedia CIRP, 104, 1936-1941. https://doi.org/10.1016/j.procir.2021.11.327

Macedo, H. D. & Pierce, K. (red.) (2022). Proceedings of the 20th International Overture Workshop. https://doi.org/10.48550/arXiv.2208.10233

Macedo, H. D. & Touili, T. (2013). Mining Malware Specifications through Static Reachability Analysis.

Macedo, H. D. & Oliveira, J. N. (2013). Typing linear algebra: A biproduct-oriented approach.

Macedo, H. D. & Pierce, K. (2023). Proceedings of the 21st International Overture Workshop. http://arxiv.org/pdf/2311.07120v2

Maalandish, M., Babaeian, E., Abolhasani, P., Gheisarnejad Chirani, M. & Khooban, M. H. (2023). Ultra high step-up soft-switching DC/DC converter using coupled inductor and interleaved technique. IET Power Electronics, 16(8), 1320-1338. https://doi.org/10.1049/pel2.12472

Maalandish, M., Hosseini, S. H., Sabahi, M., Rostami, N. & Khooban, M. H. (2023). B-MIMO high step-up DC/DC converter with high capability to control inputs. IET Power Electronics, 16(9), 1499-1513. https://doi.org/10.1049/pel2.12486

Maalandish, M., Hosseini, S. H., Sabahi, M., Rostami, N. & Khooban, M. H. (2023). High step-up multi input–multi output DC–DC converter with high controllability for battery charger/EV applications. IET Power Electronics, 16(15), 2606-2623. https://doi.org/10.1049/pel2.12587

Maalandish, M., Hosseini, S. H., Sabahi, M., Rostami, N. & Khooban, M. H. (2024). Soft-switching multiport DC-DC converter for PV/EV applications with a bidirectional port. IET Renewable Power Generation, 18(12), 1862-1879. https://doi.org/10.1049/rpg2.13055

Ma, C., Rutz, D., Hofmeier, V., Janssen, R., Balafoutis, A., Vaiopoulos, K., Vandorou, F., Sørensen, C. A. G., Manolakos, D., Koutsouris, A., Paris, B., Papadakis, G., Borzecka, M., Wydra, M., Biševac, V., Creupelandt, D., Román, J., Rossi, D., Próchniak, M. ... Asín, M. (2022). Support for Decarbonizing the European Agriculture - The Agrofossilfree Project. I European Biomass Conference and Exhibition Proceedings (EUBCE 2022) (s. 402-405). ETA-Florence Renewable Energies. http://www.etaflorence.it/proceedings/

Ma, C., Rutz, D., Balafoutis, A., Vaiopoulos, K., Vandorou, F., Kyriakarakos, G., Grøn Sørensen, C., Nørremark, M., Kristensen, E. F., Manolakos, D., Koutsouris, A., Kanaki, V., Paris, B., Papadakis, G., Borzecka, M., Wydra, M., Biševac, V., Vansintjan, D., Román, J. ... Hansen, A. G. (2023). SUPPORTING BIOMASS USAGE AND PRODUCTION TO DECARBONIZE AGRICULTURE INDUSTRY: THE AGROFOSSILFREE PROJECT. I 31st European Biomass Conference and Exhibition: Proceedings (s. 373 - 378)

Lyng, R., Bennedsen, J., Bettaieb, L., Bodsberg, N. R., Edström, K., Guðjónsdóttir, M. S., Roslöf, J., Solbjørg, O. K. & Øien, G. (2023). Editorial. I Proceedings of the 19th International CDIO Conference (s. iv-v). NTNU Norwegian University of Science and Technology.

Lund, J., Jensen, L. B., Macedo, H. D. & Larsen, P. G. (2022). Towards UML and VDM Support in the VS Code Environment. I H. D. Macedo & K. Pierce (red.), Proceedings of the 20th International Overture Workshop (s. 50-65) https://doi.org/10.48550/arXiv.2208.10233

Lund, J., Jensen, L. B., Battle, N., Larsen, P. G. & Macedo, H. D. (2023). Bidirectional UML Visualisation of VDM Models.

Lumer-Klabbers, G., Hausted, J. O., Kvistgaard, J. L., Macedo, H. D., Frasheri, M. & Larsen, P. G. (2021). Towards a digital twin framework for autonomous robots. I W. K. Chan, B. Claycomb, H. Takakura, J.-J. Yang, Y. Teranishi, D. Towey, S. Segura, H. Shahriar, S. Reisman & S. I. Ahamed (red.), 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC) (s. 1254-1259). IEEE. https://doi.org/10.1109/COMPSAC51774.2021.00174

Lotlikar, V. S., Satpute, N. & Gupta, A. (2022). Brain Tumor Detection Using Machine Learning and Deep Learning: A Review. Current Medical Imaging, 18(6), 604-622. https://doi.org/10.2174/1573405617666210923144739

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