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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Nørremark, M., Hansen, M. J., Kai, P., Børsting, C. F., Ottosen, C.-O. & Konnerup, D., (2023). Notat vedrørende miljø- og klimateknologier 2023 – del 1, Nr. 2023-0505187, 21 s., jun. 02, 2023. Rådgivningsnotat fra DCA - Nationalt Center for Fødevarer og Jordbrug

Nørremark, M., Hansen, M. J., Kai, P., Børsting, C. F. & Konnerup, D., (2024). Notat vedrørende besvarelse af henvendelser under Miljø- og Klimateknologi 2023, Nr. 2023-0613092, 9 s., feb. 21, 2024. Rådgivningsnotat fra DCA - Nationalt Center for Fødevarer og Jordbrug

Hurst, A., Kalinichev, A. V., Koren, K. & Lucani, D. E. (2026). Not All Those Who Drift Are Lost: Drift Correction and Calibration Scheduling for the IoT. IEEE Internet of Things Journal, 13(2), 2144-2154. https://doi.org/10.1109/JIOT.2025.3628204

Dormann, F., Frisk, O., Andersen, L. N. & Pedersen, C. F. (2021). Not All Noise Is Accounted Equally: How Differentially Private Learning Benefits From Large Sampling Rates. I 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021 IEEE. https://doi.org/10.1109/MLSP52302.2021.9596307

Richter, S., Khatiboun, D. F., Sadeghi, M., Zamani, M. & Moradi, F. (2025). Non-uniform Memory Partitioning For Low-Power Spiking Neural Networks. I AICAS 2025 - 2025 7th IEEE International Conference on Artificial Intelligence Circuits and Systems, Proceedings IEEE. https://doi.org/10.1109/AICAS64808.2025.11173151

Abrazeh, S., Parvaresh, A., Mohseni, S.-R., Jahanshahi Zeitouni, M., Gheisarnejad, M. & Khooban, M. H. (2021). Nonsingular Terminal Sliding Mode Control With Ultra-Local Model and Single Input Interval Type-2 Fuzzy Logic Control for Pitch Control of Wind Turbines. IEEE/CAA Journal of Automatica Sinica, 8(3), 690-700. Artikel 9346074. https://doi.org/10.1109/JAS.2021.1003889

Johansen, K. W., Nielsen, R. O., Schultz, C. P. L. & Teizer, J. (2020). Non-monotonic Reasoning For Automated Progress Analysis Of Construction Operations. I N. Dawood , F. Pour Rahimian, S. Seyedzadeh & M. Sheikhkhoshkar (red.), Enabling the Development and Implementation of Digital Twins: Proceedings of the 20th International Conference on Construction Applications of Virtual Reality (s. 292-303) https://www.researchgate.net/profile/Farzad-Pour-Rahimian/publication/344444905_Enabling_the_Development_and_Implementation_of_Digital_Twins_Proceedings_of_the_20th_International_Conference_on_Construction_Applications_of_Virtual_Reality/links/5fb3ec4445851518fdad5eab/Enabling-the-Development-and-Implementation-of-Digital-Twins-Proceedings-of-the-20th-International-Conference-on-Construction-Applications-of-Virtual-Reality.pdf

Gravesen, K. B., Gardner, A. B., Ulsig, E. Z., Stanton, E. J., Hansen, M. T., Thomsen, S. T., Ahler, L. & Volet, N. (2024). Nonlinear Schrödinger equation for integrated photonics. Journal of the Optical Society of America B: Optical Physics, 41(6), 1451-1456. https://doi.org/10.1364/JOSAB.517190

Huang, Q., Li, H., Liao, Y., Hao, Y. & Zhou, P. (2024). Noise-NeRF: Hide Information in Neural Radiance Field Using Trainable Noise. I M. Wand, K. Malinovská, J. Schmidhuber & I. V. Tetko (red.), Artificial Neural Networks and Machine Learning – ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Proceedings (s. 320-334). Springer. https://doi.org/10.1007/978-3-031-72335-3_22

Sadeghi, M., Rezaeiyan, Y., Fernández Khatiboun, D., Eissa, S., Corradi, F., Augustine, C. & Moradi, F. (2025). NEXUS: A 28nm 3.3pJ/SOP 16-Core Spiking Neural Network with a Diamond Topology for Real-Time Data Processing. IEEE Transactions on Biomedical Circuits and Systems, 19(3), 523-535. https://doi.org/10.1109/TBCAS.2024.3452635

Bozorgasli, Z., Foroozan, H., Gheisarnejad Chirani, M. & Khooban, M. H. (2021). New Trends on Digital Twin-Based Blockchain Technology in Zero-Emission Ship Applications. Naval Engineers Journal, 133(3), 115-135. http://bt.e-ditionsbyfry.com/publication/?m=63540&i=721756&view=articleBrowser&article_id=4109541&ver=html5

Gomes Guerreiro, G. M., Martin, F., Yang, G. & Andresen, B. (2022). New pathways to future grid compliance for wind power plants. 115-121. https://doi.org/10.1049/icp.2022.2745

Dobbs, A. M., Ginn, D., Skovsen, S. K., Bagavathiannan, M. V., Mirsky, S. B., Reberg-Horton, C. S. & Leon, R. G. (2022). New directions in weed management and research using 3D imaging. Weed Science, 70(6), 641-647. https://doi.org/10.1017/wsc.2022.56

Yildirim, B., Razmi, P., Fathollahidehkordi, A., Gheisarnejad Chirani, M. & Khooban, M. H. (2023). Neuromorphic deep learning frequency regulation in stand-alone microgrids. Applied Soft Computing, 144, Artikel 110418. https://doi.org/10.1016/j.asoc.2023.110418

Khatiboun, D. F. (2026). Neuromorphic Computing on the Edge On the Design of Energy-efficient and Adaptive Spiking Neural Network Architectures for Real-time Devices. [Ph.d.-afhandling, Aarhus Universitet].

Borges, H. B. (2025). Neural Correlates of Speech Intelligibility. [Ph.d.-afhandling, Aarhus Universitet].

Bliddal, H., Christensen, C. B., Møller, C., Vuust, P. & Kidmose, P. (2021). Neural correlates of beat perception measured using ear-EEG: Bringing EEG music studies into the concert hall . Poster-session præsenteret på Rhythm Production and Perception Workshop 2021, Oslo, Norge.

Bliddal, H., Christensen, C. B., Møller, C., Vuust, P. & Kidmose, P. (2021). Neural correlates of beat perception measured using ear-EEG. Poster-session præsenteret på Advances and Perspectives in Auditory Neuroscience, USA.

Zhao, X. (2020). Network Coding for Distributed Storage of IoT. [Ph.d.-afhandling, Aarhus Universitet].

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

Ronchini, M., Rezaeiyan, Y., Zamani, M., Panuccio, G. & Moradi, F. (2022). NET-TEN: a silicon neuromorphic network for low-latency detection of seizures in local field potentials. ArXiv. https://doi.org/10.48550/arXiv.2210.10565

Ronchini, M., Rezaeiyan, Y., Zamani, M., Panuccio, G. & Moradi, F. (2023). NET-TEN: a silicon neuromorphic network for low-latency detection of seizures in local field potentials. Journal of Neural Engineering, 20(3), Artikel 036002. https://doi.org/10.1088/1741-2552/acd029

Böttner, C., Jakobsen, F. W., Nielsen, T., Winsborrow, M., Polteau, S., Mazzini, A., Planke, S., Andresen, K. J., Millinge, O. J. S., Asif, M. R., Laberg, J. S., Hopper, J., Myklebust, R. & Seidenkrantz, M. S. (2025). Natural hydrocarbon seepage at the Northeast Greenland continental shelf. Communications Earth and Environment, 6(1), Artikel 879. https://doi.org/10.1038/s43247-025-02932-8

Naveed, K., Mukhtar, S. & Ur Rehman, N. (2021). Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis. I 2021 IEEE Statistical Signal Processing Workshop, SSP 2021 (s. 441-445). IEEE. https://doi.org/10.1109/SSP49050.2021.9513823

Ahmed, M. U., Rehman, N., Looney, D., Rutkowski, T. M., Kidmose, P. & Mandic, D. P. (2012). Multivariate entropy analysis with data-driven scales. I 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (s. 3901-3904). IEEE. https://doi.org/10.1109/icassp.2012.6288770, https://doi.org/10.1109/ICASSP.2012.6288770

Wang, X., Berardinelli, G., Cheng, H. V., Popovski, P. & Adeogun, R. (2025). Multi-User Beamforming with Deep Reinforcement Learning in Sensing-Aided Communication. 61-66. https://doi.org/10.1109/EuCNC/6GSummit63408.2025.11036884

Shi, L., Hernandez Marcano, N. J. & Jacobsen, R. H. (2022). Multi-UAV Path Coordination through Generalized Potential Games. I 2022 8th International Conference on Automation, Robotics and Applications, ICARA 2022 (s. 125-129). IEEE. https://doi.org/10.1109/ICARA55094.2022.9738578

Morante Moreno, M., Frølich, K., Shahzaib, M., shakil, S. & Rehman, N. U. (2024). Multiscale Functional Connectivity analysis of episodic memory reconstruction. Frontiers in Cognition, 3, Artikel 1433234. https://doi.org/10.3389/fcogn.2024.1433234

Nazari, M., Korshoej, A. R. & Rehman, N. U. (2024). Multiscale Dynamic Graph Signal Analysis. Social Science Research Network (SSRN). https://doi.org/10.2139/ssrn.4713829

Nazari, M., Korshøj, A. R. & ur Rehman, N. (2024). Multiscale dynamic graph signal analysis. Signal Processing, 222, Artikel 109519. https://doi.org/10.1016/j.sigpro.2024.109519

Dahl, M. K., Hassan Hire, J., Zamani, M. & Moradi, F. (2024). Multimodal Temperature Sensing Using Electromechanical Impedance (EMI) for Structural Health Monitoring. IEEE Sensors Journal, 24(3), 3457-3464. https://doi.org/10.1109/JSEN.2023.3343779

Gheisarnejad Chirani, M. & Khooban, M. H. (2021). Multi-microgrids with a Frequency Regulation-Based V2G Technology: Systems Analysis, Modeling, and Control. I Design, Control, and Operation of Microgrids in Smart Grids: Power Systems (s. 1-26). Springer. https://doi.org/10.1007/978-3-030-64631-8_1

Fathollahidehkordi, A. & Andresen, B. (2023). Multi-Machine Power System Transient Stability Enhancement Utilizing a Fractional Order-Based Nonlinear Stabilizer. Fractal and Fractional, 7(11), 808. Artikel 808. https://doi.org/10.3390/fractalfract7110808

Tran, D. T., Yamac, M., Degerli, A., Gabbouj, M. & Iosifidis, A. (2021). Multilinear compressive learning. IEEE Transactions on Neural Networks and Learning Systems, 32(4), 1512-1524. Artikel 9070152. https://doi.org/10.1109/TNNLS.2020.2984831

Guven, Z. A., Mendes, E. & Baker, S. (2026). Multi-label text classification using prompting techniques via LLMs in Software Effort Estimation. IEEE Access, 14. https://doi.org/10.1109/ACCESS.2026.3679475

Shabani, M., Tran, D. T., Magris, M., Kanniainen, J. & Iosifidis, A. (2022). Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction. I 30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings (s. 1487-1491). IEEE. https://doi.org/10.23919/EUSIPCO55093.2022.9909957

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

Fathollahidehkordi, A., Gheisarnejad Chirani, M., Boudjadar, J., Derakhshandeh, S. Y. & Khooban, M. H. (2023). Multi-Criteria Design of Electric Transit Bus Based on Wireless Charging Infrastructure: A Case Study of Real Road Map in Wakefield. Automation , 4(3), 291-308. https://doi.org/10.3390/automation4030017

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. Afhandling præsenteret på 2023 Joint EuCNC & 6G Summit, Gothenburg, Sverige.

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

Sejersen, J. L. F., Sarabakha, A., Plagborg Bak Sørensen, K. & Høigaard Jensen, J. (2025). Multi-Agent Path Planning in Complex Environments using Gaussian Belief Propagation with Global Path Finding. 4359-4365. Afhandling præsenteret på 2025 IEEE International Conference on Robotics and Automation (ICRA) , Atlanta, Georgia, USA. https://doi.org/10.1109/ICRA55743.2025.11128006

Yildirim, B., Gheisarnejad Chirani, M., Temel Özdemir, M. T. Ö. & Khooban, M. H. (2024). Multi-agent fuzzy Q-learning-based PEM fuel cell air-feed system control. International Journal of Hydrogen Energy, 75, 354-362. https://doi.org/10.1016/j.ijhydene.2024.02.129

Nørremark, M., (2024). Muligheder for miljøeffektberegning af teknologier for pesticidreduktion i relation til økologisk jordbrug, Nr. 2024-0751020, 4 s., nov. 29, 2024. Rådgivningsnotat fra DCA - Nationalt Center for Fødevarer og Jordbrug

Nørremark, M. (2024). Muligheder for mere sikker håndtering af kvæg ved teknologisk innovation. DCA - Nationalt Center for Fødevarer og Jordbrug.

Kazemi, Z., Rask, J. K., Gomes, C., Yildiz, E. & Larsen, P. G. (2023). Movable factory—A systematic literature review of concepts, requirements, applications, and gaps. Journal of Manufacturing Systems, 69, 189-207. https://doi.org/10.1016/j.jmsy.2023.06.008

Sadiq, M. T., Yu, X., Yuan, Z., Aziz, M. Z., Rehman, N. U., Ding, W. & Xiao, G. (2022). Motor Imagery BCI Classification Based on Multivariate Variational Mode Decomposition. IEEE Transactions on Emerging Topics in Computational Intelligence, 6(5), 1177-1189. https://doi.org/10.1109/tetci.2022.3147030

Iraji, M., Dehghani, M., Mohammadi, M., Vafamand, N. & Boudjadar, J. (2021). Motor Current Signature Analysis Using Shapelet. I H. Selvaraj, G. Chmaj & D. Zydek (red.), Proceedings of the 27th International Conference on Systems Engineering, ICSEng 2020 (s. 23-33). Springer. https://doi.org/10.1007/978-3-030-65796-3_3

Shi, L. (2022). Motion Planning for Autonomous Aerial Robots. [Ph.d.-afhandling, Aarhus Universitet]. Aarhus Universitet.

Bjerge, K., Frigaard, C. E. & Karstoft, H. (2022). Motion Informed Object Detection of Small Insects in Time-lapse Camera Recordings. arxiv.org. https://arxiv.org/abs/2212.00423

Wang, Z., Chen, L., Chen, H. & Rehman, N. U. (2023). Monthly ship price forecasting based on multivariate variational mode decomposition. Engineering Applications of Artificial Intelligence, 125, Artikel 106698. https://doi.org/10.1016/j.engappai.2023.106698

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