Publications at Department of Electrical and Computer Engineering

Under publication list you can find a complete list of the publications that are written by employees at the Publications at Department of Electrical and Computer Engineering.

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Barzegar Gerdroodbary, M. & Salavatidezfouli, S. (2025). A predictive surrogate model of blood haemodynamics for patient-specific carotid artery stenosis. Journal of the Royal Society Interface, 22(224), Article 20240774. https://doi.org/10.1098/rsif.2024.0774

Bano, F., Rizwan, A., Serbaya, S. H., Hasan, F., Karavas, C. S. & Fotis, G. (2024). Integrating Microgrids into Engineering Education: Modeling and Analysis for Voltage Stability in Modern Power Systems. Energies, 17(19), Article 4865. https://doi.org/10.3390/en17194865

Banluesombatkul, N., Strøm, J., Olsen, M. L. S., Kilic, K., Sprehn, M., Thomsen, M. D., Christian Hemmsen, M., Thorlund, J. B., Mikkelsen, K., Vægter, H. B. & Kidmose, P. (2025). Sleep Analysis Using Longitudinal Ear-EEG Recordings. In 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2025 - Proceedings IEEE. https://doi.org/10.1109/EMBC58623.2025.11251745

Banhazi, T., Banhazi, A., Tikasz, I. E., Palotay, S., Mallimger, K., Neubauer, T., Corpaci, L., Marchaim, U., Kopler, I., Opalinski, S., Olejnik, K., Kokin, E., Gunnarsson, S., Bjerre, T. & Soerensen, C. (2024). Facilitating PLF Technology Adoption in the Pig and Poultry Industries. Studies in Agricultural Economics, 126(1), 43-49. https://doi.org/10.7896/j.2725

Bandaru, N., Reddy, C. V., Vallabhudasu, K., Vijayalakshmi, M., Raghava Reddy, K., Cheolho, B., Shim, J. & Aminabhavi, T. M. (2024). Exploring the potential of MXene nanohybrids as high-performance anode materials for lithium-ion batteries. Chemical Engineering Journal, 500, Article 157317. https://doi.org/10.1016/j.cej.2024.157317

Banaei, M., Boudjadar, J. & Khooban, M. H. (2021). Stochastic Model Predictive Energy Management in Hybrid Emission-Free Modern Maritime Vessels. IEEE Transactions on Industrial Informatics, 17(8), 5430-5440. https://doi.org/10.1109/TII.2020.3027808

Banaei, M., Oloomi‐buygi, M., Raouf‐Sheybani, H. & Khooban, M. H. (2021). Mixed supply function‐Cournot equilibrium model of futures and day‐ahead electricity markets. IET Generation, Transmission & Distribution, 15(10), 1640-1654. https://doi.org/10.1049/gtd2.12123

Banaei, M., Boudjadar, J., Dragicevic, T. & Khooban, M. H. (2020). Cost Effective Operation of a Hybrid Zero-Emission Ferry Ship. In 2020 IEEE 11th International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2020 (pp. 23-28). Article 9244456 IEEE. https://doi.org/10.1109/PEDG48541.2020.9244456

Banaei, M., Boudjadar, J. & Khooban, M. H. (2021). Optimal Energy Resources Scheduling of Hybrid Diesel/Battery Ships in Shallow Waters. In H. Selvaraj, G. Chmaj & D. Zydek (Eds.), Proceedings of the 27th International Conference on Systems Engineering, ICSEng 2020 (pp. 369-378). Springer. https://doi.org/10.1007/978-3-030-65796-3_36

Banaei, M., Ghanami, F., Khooban, M. H. & Boudjadar, J. (2021). Cost-effective control of Roll-on/Roll-off Emission-Free Ships. In 2021 25th International Conference on Methods and Models in Automation and Robotics, MMAR 2021 (pp. 315-320). IEEE. https://doi.org/10.1109/MMAR49549.2021.9528473

Banaei, M., Boudjadar, J., Ebrahimy, R. & Madsen, H. (2021). Optimal Control Strategies of Fuel cell/Battery Based Zero-Emission Ships: A Survey. In IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society IEEE. https://doi.org/10.1109/IECON48115.2021.9589512

Baltakys, K., Baltakienė, M., Heidari, N., Iosifidis, A. & Kanniainen, J. (2023). Predicting the trading behavior of socially connected investors: Graph neural network approach with implications to market surveillance. Expert Systems with Applications, 228, Article 120285. https://doi.org/10.1016/j.eswa.2023.120285

Balauroiu, M., Ruf, F., Volet, N. & Heck, M. (2021). Low-loss all-optical ns-switching for scalable quantum photonic links. Poster session presented at Photonic Links for Quantum Technology Platforms.

Balauroiu, M., Ruf, F., Volet, N. & Heck, M. (2021). Ultralow loss, fast all-optical scalable switches. In 2021 IEEE Photonics Conference, IPC 2021 - Proceedings IEEE. https://doi.org/10.1109/IPC48725.2021.9593084

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2021). Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning. In 2021 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/IJCNN52387.2021.9533875

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2021). Multi-Exit Vision Transformer for Dynamic Inference. In The 32nd British Machine Vision Conference https://www.semanticscholar.org/paper/Multi-Exit-Vision-Transformer-for-Dynamic-Inference-Bakhtiarnia-Zhang/26f61039857423c0aad2c44f0e7769cbb61246c9

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2022). Single-layer vision transformers for more accurate early exits with less overhead. Neural Networks, 153, 461-473. https://doi.org/10.1016/j.neunet.2022.06.038

Bakhtiarnia, A., Leporowski, B., Esterle, L. & Iosifidis, A. (2022). Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications. In 2022 IEEE International Smart Cities Conference (ISC2) IEEE. https://doi.org/10.1109/ISC255366.2022.9921939

Bakhtiarnia, A., Milošević, N., Zhang, Q., Bajović, D. & Iosifidis, A. (2023). Dynamic Split Computing for Efficient Deep Edge Intelligence. In ICASSP 2023: 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) IEEE. https://doi.org/10.1109/ICASSP49357.2023.10096914

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2023). PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks. In 2023 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/IJCNN54540.2023.10191962

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2024). ACCURATE GIGAPIXEL CROWD COUNTING BY ITERATIVE ZOOMING AND REFINEMENT. In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6510-6514). IEEE. https://doi.org/10.1109/ICASSP48485.2024.10446065

Bakhtiarnia, A. (2024). Optimizing Deep Learning Inference for Edge Intelligence. [PhD dissertation, Aarhus University]. Aarhus University.

Bakhshaei, K., Salavatidezfouli, S., Stabile, G. & Rozza, G. (2025). Stochastic parameter prediction in cardiovascular problems. Computer Methods in Biomechanics and Biomedical Engineering. Advance online publication. https://doi.org/10.1080/10255842.2025.2569819

Bajovic, D., Bakhtiarnia, A., Bravos, G., Brutti, A., Burkhardt, F., Cauchi, D., Chazapis, A., Cianco, C., Dall'Asen, N., Delic, V., Dimou, C., Djokic, D., Escobar-Molero, A., Esterle, L., Eyben, F., Farella, E., Festi, T., Geromitsos, A., Giakoumakis, G. ... Zammit, J. (2021). MARVEL: Multimodal Extreme Scale Data Analytics for Smart Cities Environments. In 2021 International Balkan Conference on Communications and Networking, BalkanCom 2021 (pp. 143-147). IEEE. https://doi.org/10.1109/BalkanCom53780.2021.9593258

Bagheri Tookanlou, P., Pourmoayed, R., Grøn Sørensen, C. & Nørremark, M. (2025). Predicting readiness of arable land and scheduling of tillage operations using finite-horizon Markov decision process and Gaussian state space models. The Open Operational Research Journal, 25(4), Article 92. https://doi.org/10.1007/s12351-025-00964-8

Baghaee Ivriq, S., Laursen, K., Møller Jørgensen, A., Mondal, T., Zamani, M., Rezaeiyan, Y., Corbett, B., Iversen, B. B. & Moradi, F. (2024). A System-Level Feasibility Study of a Lead-Free Ultrasonically Powered Light Delivery Implant for Optogenetics. Advanced Intelligent Systems , 6(3), Article 2300527. https://doi.org/10.1002/aisy.202300527

Baghaee Ivriq, S., Mohammadi, M. & Davidsen, R. S. (2025). Enhancing photovoltaic efficiency in Half-Tandem MAPbI3/ MASnI3 Perovskite solar cells with triple core-shell plasmonic nanoparticles. Scientific Reports, 15(1), Article 1478. https://doi.org/10.1038/s41598-025-85243-4

Badicu, A., Iordache, G., Suciu, G., Macedo, H. D., Sassanelli, C., Terzi, S. & Larsen, P. G. (2021). Deploying the Smart Energy Tool for Investment Simulation inside the HUBCAP Sandbox. In A. G. Bruzzone, J. S. Janosy, L. Nicoletti & G. Zacharewicz (Eds.), 9th International Workshop on Simulation for Energy, Sustainable Development and Environment, SESDE 2021 (pp. 18-26) https://doi.org/10.46354/i3m.2021.sesde.003

Badenhorst, W., Jensen, C. M., Jakobsen, U., Esfahani, Z. & Murtomäki, L. (2023). Control-Oriented Electrochemical Model and Parameter Estimation for an All-Copper Redox Flow Battery. Batteries, 9(5), Article 272. https://doi.org/10.3390/batteries9050272

Aubard, M., Quijano, S., Alvarez Tunon, O., Antal, L. & Brodskiy, Y. (2024). Mission Planning and Safety Assessment for Pipeline Inspection Using Autonomous Underwater Vehicles: A Framework based on Behavior Trees. In OCEANS 2024 - Singapore IEEE. https://doi.org/10.1109/OCEANS51537.2024.10682385

Asplund, F., Macedo, H. D. & Sassanelli, C. (2021). Problematizing the Service Portfolio of Digital Innovation Hubs. In L. M. Camarinha-Matos, X. Boucher & H. Afsarmanesh (Eds.), Smart and Sustainable Collaborative Networks 4.0 - 22nd IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2021, Proceedings: 22nd IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2021, Saint-Étienne, France, November 22–24, 2021, Proceedings (pp. 433-440). Springer. https://doi.org/10.1007/978-3-030-85969-5_40

Asiminari, G., Moysiadis, V., Kateris, D., Busato, P., Wu, C., Achillas, C., Sørensen, C. G., Pearson, S. & Bochtis, D. (2024). Integrated Route-Planning System for Agricultural Robots. AgriEngineering, 6(1), 657-677. https://doi.org/10.3390/agriengineering6010039

Asiminari, G., Benos, L., Kateris, D., Busato, P., Achillas, C., Grøn Sørensen, C., Pearson, S. & Bochtis, D. (2025). Simplifying Field Traversing Efficiency Estimation Using Machine Learning and Geometric Field Indices. AgriEngineering, 7(3), Article 75. https://doi.org/10.3390/agriengineering7030075

Asif, M. R., Bording, T. S., Barfod, A. S., Grombacher, D. J., Maurya, P. K., Christiansen, A. V., Auken, E. & Larsen, J. J. (2021). Effect of Data Pre-Processing on the Performance of Neural Networks for 1-D Transient Electromagnetic Forward Modeling. IEEE Access, 9, 34635-34646. https://doi.org/10.1109/ACCESS.2021.3061761

Asif, M. R., Bording, T. S., Maurya, P. K., Zhang, B., Fiandaca, G., Grombacher, D. J., Christiansen, A. V., Auken, E. & Larsen, J. J. (2022). A Neural Network-Based Hybrid Framework for Least-Squares Inversion of Transient Electromagnetic Data. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 4503610. https://doi.org/10.1109/TGRS.2021.3076121

Asif, M. R., Larsen, J. J., Maurya, P. K., Christiansen, A. V. & Auken, E. (2021). Complete automation of time-domain transient electromagnetic data processing using deep convolutional autoencoder. Abstract from AGU 2021, United States.

Asif, M. R., Foged, N., Maurya, P. K., Grombacher, D. J., Christiansen, A. V., Auken, E. & Larsen, J. J. (2022). Integrating neural networks in least-squares inversion of airborne time-domain electromagnetic data. Geophysics, 87(4), E177-E187. https://doi.org/10.1190/geo2021-0335.1

Asif, M. R., Maurya, P. K., Foged, N., Larsen, J. J., Auken, E. & Christiansen, A. V. (2022). Automated transient electromagnetic data processing for ground-based and airborne systems by a deep learning expert system. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 5919814. https://doi.org/10.1109/TGRS.2022.3202304

Asif, M. R., Foged, N., Bording, T. S., Larsen, J. J. & Christiansen, A. V. (2023). DL-RMD: a geophysically constrained electromagnetic resistivity model database for deep learning applications. Earth System Science Data, 15(3), 1389-1401. https://doi.org/10.5194/essd-2022-345, https://doi.org/10.5194/essd-15-1389-2023

Asif, M. R., Larsen, J. J., Auken, E. & Christiansen, A. V. (2022). Deep Convolutional Auto-Encoder for Automated Processing of Airborne Time-Domain Electromagnetic Data. 1-5. Paper presented at NSG2022 3rd Conference on Airborne, Drone and Robotic Geophysics, Beograd, Serbia. https://doi.org/10.3997/2214-4609.202220038

Asif, M. R., Maurya, P. K., Larsen, J. J. & Christiansen, A. V. (2022). Investigating the Integration of Neural Networks in Least-Squares Method for Airborne Electromagnetic Data Inversion. 1-5. Paper presented at NSG2022 3rd Conference on Airborne, Drone and Robotic Geophysics, Beograd, Serbia. https://doi.org/10.3997/2214-4609.202220034

Asif, M. R., Kass, A., Westerhoff, R., Rawlinson, Z., Christiansen, A. V. & Bording, T. S. (2023). Automated Processing of a Large-Scale Airborne Electromagnetic Survey by Deep Learning. Paper presented at NSG2023 29th European Meeting of Environmental and Engineering Geophysics, Edinburgh , United Kingdom. https://doi.org/10.3997/2214-4609.202320057

Asif, M. R., Kass, A., Westerhoff, R., Rawlinson, Z. & Christiansen, A. V. (2023). Automated data processing of a large-scale airborne time-domain electromagnetic survey by a deep learning expert system. Paper presented at 8th International Workshop on Airborne Electromagnetics, Queensland, Australia.

Asif, M. R., Maurya, P. K., Foged, N. & Christiansen, A. V. (2023). A DATA DRIVEN APPROACH FOR ROBUST INVERSION OF INDUCED POLARIZATION EFFECTS IN TRANSIENT ELECTROMAGNETIC DATA. In APPLICATION OF GEOPHYSICS TO ENGINEERING AND ENVIRONMENTAL PROBLEMS: SYMPOSIUM. 35TH 2023. (SAGEEP 2023) (pp. 82-82). Curran Associates.

Asif, M. R., Kass, M. A., Herpe, M., Rawlinson, Z., Westerhoff, R., Larsen, J. J. & Christiansen, A. V. (2025). Comparative analysis of deep learning and traditional airborne electromagnetic data processing: A case study. Geophysics, 90(3), WA103-WA112. https://doi.org/10.1190/geo2024-0282.1

Asif, MR., Larsen, JJ. & Christiansen, AV. (2024). Automated Processing of Time-Domain Electromagnetic Data Influenced by Induced Polarization Effects. In NSG 2024 30th European Meeting of Environmental and Engineering Geophysics (pp. 1-5). European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.202420140

Asif, M. R. (2025). Benchmarking Deep Learning for Wetland Mapping in Denmark Using Remote Sensing Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 11953-11962. https://doi.org/10.1109/JSTARS.2025.3563951

Asif, M. R., Kass, A., Larsen, J. J. & Christiansen, A. V. (2025). Identifying Induced Polarization Effects in TEM Data by Deep Learning. In 7th Asia Pacific Meeting on Near Surface Geoscience and Engineering, NSGE 2025 (pp. 1-5). European Association of Geoscientists and Engineers, EAGE. https://doi.org/10.3997/2214-4609.202572056

Asif, M. R., Rafiei Foroushani, M., Jørgensen, R. N., Nørremark, M. & Teimouri, N. (2025). Assessing generalization of deep learning models for crop classification under climatic variability in Denmark. Abstract from EGU General Assembly 2025, Vienna, Austria. https://doi.org/10.5194/egusphere-egu25-9634

Asif, M. R., Kass, A., Larsen, J. J. & Christiansen, A. V. (2024). Identification of Induced Polarization affected Transient Electromagnetic Data by Deep Learning. Poster session presented at AGU Fall meeting 2024, Washington DC, United States. https://ui.adsabs.harvard.edu/abs/2024AGUFMNS23B1231A/abstract

Displaying results 1301 to 1350 out of 1435

Revised 30.12.2025

AU Engineering