Department of Electrical and Computer Engineering

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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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Polinski, A., Mazur-Milecka, M., Kowalczyk, N., Jaguszewska, K. & Wagner, S. R. (2025). Local Pulse Transit Time Analysis. In Proceeding - 17th International Conference on Human System Interaction, HSI 2025 IEEE Computer Society Press. https://doi.org/10.1109/HSI66212.2025.11142434
Piozin, C., Fares, H. & Moradi, F. (2025). Subject-Specific Parameter Optimization for Event-Driven Encoding of sEMG in Regression-Based Motor Control. In 2025 32nd IEEE International Conference on Electronics, Circuits and Systems, ICECS 2025 IEEE. https://doi.org/10.1109/ICECS66544.2025.11270549
Piozin, C., Khatiboun, D. F. & Moradi, F. (2025). Event-driven Processing of EEG using LIF-Based Flagging for Efficient Seizure Detection. In Proceedings - 21st IEEE Biomedical Circuits and Systems, BioCAS 2025 (pp. 606-610). IEEE. https://doi.org/10.1109/BioCAS67066.2025.00142
Pimentel de Figueiredo, R., Le Fevre Sejersen, J., Grimm Hansen, J. & Brandão, M. (2022). Integrated design-sense-plan architecture for autonomous geometric-semantic mapping with UAVs. Frontiers in Robotics and AI, 9, Article 911974. https://doi.org/10.3389/frobt.2022.911974
Pierce, K., Lausdahl, K. & Frasheri, M. (2022). Speeding Up Design Space Exploration through Compiled Master Algorithms. In H. D. Macedo & K. Pierce (Eds.), Proceedings of the 20th International Overture Workshop (pp. 66-81) https://doi.org/10.48550/arXiv.2208.10233
Pianini, D., Pettinari, F., Casadei , R. & Esterle, L. (2022). A Collective Adaptive Approach to Decentralised k-Coverage in Multi-robot Systems. ACM Transactions on Autonomous and Adaptive Systems, 17(1-2), 1-39. Article 4. https://doi.org/10.1145/3547145
Phan, H. & Mikkelsen, K. (2022). Automatic sleep staging of EEG signals: recent development, challenges, and future directions. Physiological Measurement, 43(4), Article 04TR01. https://doi.org/10.1088/1361-6579/ac6049
Phan, H., Mikkelsen, K., Chen, O. Y., Koch, P., Mertins, A. & De Vos, M. (2022). SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification. IEEE Transactions on Biomedical Engineering, 69(8), 2456-2467. https://doi.org/10.1109/TBME.2022.3147187
Phan, H., Lorenzen, K. P., Heremans, E., Chen, O. Y., Tran, M. C., Koch, P., Mertins, A., Baumert, M., Mikkelsen, K. B. & De Vos, M. (2023). L-SeqSleepNet: Whole-cycle Long Sequence Modeling for Automatic Sleep Staging. IEEE Journal of Biomedical and Health Informatics, 27(10), 4748-4757. https://doi.org/10.1109/JBHI.2023.3303197
Pham, X. H., Bozcan, I., Sarabakha, A., Haddadin, S. & Kayacan, E. (2021). GateNet: An Efficient Deep Neural Network Architecture for Gate Perception Using Fish-Eye Camera in Autonomous Drone Racing. 4176-4183. Paper presented at 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, Prag, Czech Republic. https://doi.org/10.1109/IROS51168.2021.9636207
Pham, H. X., Ugurlu, H. I., Fevre, J. L., Bardakci, D. & Kayacan, E. (2022). Deep learning for vision-based navigation in autonomous drone racing. In A. Iosifidis & A. Tefas (Eds.), Deep Learning for Robot Perception and Cognition (pp. 371-406). Elsevier. https://doi.org/10.1016/B978-0-32-385787-1.00020-8
Pham, X. H., Sarabakha, A., Odnoshyvkin, M. & Kayacan, E. (2022). PencilNet: Zero-Shot Sim-to-Real Transfer Learning for Robust Gate Perception in Autonomous Drone Racing. IEEE Robotics and Automation Letters, 7(4), 11847 - 11854. https://doi.org/10.1109/LRA.2022.3207545
Pham, X. H., Heiß, M., Tran, D., Nguyen, M. A., Nguyen, A. Q. & Kayacan, E. (2023). ORB-Net: End-to-end Planning Using Feature-based Imitation Learning for Autonomous Drone Racing. In ISR Europe 2023: 56th International Symposium on Robotics, in cooperation with Fraunhofer IPA September 26 – 27, 2023 in Stuttgart (pp. 16-21). VDE Verlag GmbH. https://ieeexplore.ieee.org/document/10363048
Pham, X. H. (2023). Efficient deep learning-based monocular 3D perception for agile robot navigation. [PhD dissertation, Aarhus University]. Aarhus Universitet.
Pham, H. X. & Kayacan, E. (2025). FROST: Fusion and Multimodal 3D Reconstruction of Icy Surfaces for Robotic Exploration. In 2025 IEEE Symposium on Computational Intelligence on Engineering/Cyber Physical Systems Companion, CIES Companion 2025 IEEE. https://doi.org/10.1109/CIESCompanion65073.2025.11010821
Peykarporsan, R., Oshnoei, S., Fathollahi, A. & Lie, T. T. (2025). A Novel Intelligent Fractional Order Cascade Control to Enhance Wind Energy Conversion in Wind Farms: A Practical Case Study. IEEE Transactions on Energy Conversion, 40(3), 1736-1749. https://doi.org/10.1109/TEC.2025.3543144
Pedersen, L., Wagner, S., Skov, H. & Sandager, P. (2023). A Time Study for the Analysis of the Potential for the Automated Stepwise Screening Program for Preeclampsia at Week 12 of Gestation. In G. A. Papadopoulos, A. Achilleos, E. Pissaloux & R. Velázquez (Eds.), ICT for Health, Accessibility and Wellbeing - 2nd International Conference, IHAW 2022, Revised Selected Papers (pp. 189-199). Springer. https://doi.org/10.1007/978-3-031-29548-5_13
Pedersen, L. T., Mazur-Milecka, M., Rumiński, J. & Wagner, S. R. (2024). A Review on Machine Learning Deployment Patterns and Key Features in the Prediction of Preeclampsia. Machine Learning and Knowledge Extraction, 6(4), 2515-2569. https://doi.org/10.3390/make6040123
Pedersen, M. B., Pelletier, L. S., Rezaeiyan, Y., Bohnert, T., Zamani, M., Shreya, S., Mosalmani, A., Paz, E., Farkhani, H., Ferreira, R. & Moradi, F. (2025). A Miniaturized, Low-Noise Magnetic Tunnel Junction (MTJ)-Based Gyroscope for High-Precision Rotation Sensing. In 2025 23rd IEEE Interregional NEWCAS Conference, NEWCAS 2025 (pp. 212-215). IEEE. https://doi.org/10.1109/NewCAS64648.2025.11107116
Pauwels, R. & Iosifidis, A. (2024). Deep Learning in Image Processing: Part 1—Types of Neural Networks, Image Segmentation. In Artificial Intelligence in Dentistry (pp. 283-316). Springer. https://doi.org/10.1007/978-3-031-43827-1_17
Pauwels, R. & Iosifidis, A. (2024). Deep Learning in Image Processing: Part 2—Image Enhancement, Reconstruction and Registration. In K. Orhan & R. Jagtap (Eds.), Artificial Intelligence in Dentistry (pp. 317-351). Springer. https://doi.org/10.1007/978-3-031-43827-1_18
Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2021). Hypersphere-Based Weight Imprinting for Few-Shot Learning on Embedded Devices. IEEE Transactions on Neural Networks and Learning Systems, 32(2), 925-930. https://doi.org/10.1109/TNNLS.2020.2979745
Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2021). Robust hypersphere-based weight imprinting for few-shot learning. In 2020 28th European Signal Processing Conference (EUSIPCO (pp. 1392-1396). Article 9287340 IEEE. https://doi.org/10.23919/Eusipco47968.2020.9287340
Passalis, N., Kanniainen, J., Gabbouj, M., Iosifidis, A. & Tefas, A. (2021). Forecasting Financial Time Series Using Robust Deep Adaptive Input Normalization. Journal of Signal Processing Systems, 93(10), 1235-1251. https://doi.org/10.1007/s11265-020-01624-0
Paredis, R., Gonçalves Gomes, C. Â. & Vangheluwe, H. (2021). Towards a Family of Digital Model/Shadow/Twin Workflows and Architectures: In H. Panetto, M. Macchi & K. Madani (Eds.), Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2021 (pp. 174-182). SCITEPRESS Digital Library. https://doi.org/10.5220/0010717600003062
Paredis, R., Gomes, C. & Vangheluwe, H. (2023). A Family of Digital T Workflows and Architectures: Exploring Two Cases. In A. Smirnov, H. Panetto & K. Madani (Eds.), Innovative Intelligent Industrial Production and Logistics: First International Conference, IN4PL 2020, Virtual Event, November 2-4, 2020, and Second International Conference, IN4PL 2021, Virtual Event, October 25-27, 2021, Revised Selected Papers (pp. 93-109). Springer. https://doi.org/10.1007/978-3-031-37228-5_6
Pajooh, B., Yildirim, B., Rouhi Ardeshiri, R., Gheisarnejad Chirani, M., Homayounzadeh, M. & Khooban, M. H. (2024). Role of redox flow battery and AI-based controller in frequency regulation of weak microgrids. Journal of Energy Storage, 84(Part B), Article 110904. https://doi.org/10.1016/j.est.2024.110904
Ourang, S. A. H., Sohrabniya, F., Zhou, P. Y., Pasqualini, D., Duncan, H. F., Pauwels, R. & Nosrat, A. (2026). Extended Reality in Endodontics: A Review of Current Applications and Future Potential. International Endodontic Journal. Advance online publication. https://doi.org/10.1111/iej.70204
Oudshoorn, F. W., Bartzanas, T., Holden, N., Järvinen, M., Jensen, A. L., Lauwers , L., Vandaele, L., Guckt, T. V. D., Van Nuffel, A., Saeys, W. & Grøn Sørensen, C. (2014). ICT-AGRI Call 2 Mid-term Project Report: SILF: Smart Integrated Livestock Farming. https://pureportal.ilvo.be/en/publications/ict-agri-call-2-mid-term-project-report-silf-smart-integrated-liv
Oshnoei, S., Fathollahidehkordi, A., Oshnoei, A. & Khooban, M. H. (2023). Microgrid Frequency Regulation Based on a Fractional Order Cascade Controller. Fractal and Fractional, 7(4), Article 343. https://doi.org/10.3390/fractalfract7040343
Oshnoei, S., Aghamohammadi, M. R., Oshnoei , S., Sahoo, S., Fathollahidehkordi, A. & Khooban, M. H. (2023). A novel virtual inertia control strategy for frequency regulation of islanded microgrid using two-layer multiple model predictive control. Applied Energy, 343, Article 121233. https://doi.org/10.1016/j.apenergy.2023.121233
Oshnoei, S., Aghamohammadi, M. R. & Khooban, M. H. (2024). Model-Free Predictive Frequency Control Under Sensor and Actuator FDI Attacks. IEEE Transactions on Circuits and Systems II: Express Briefs, 71(4), 2434-2438. https://doi.org/10.1109/TCSII.2023.3339545
Oshnoei, S., Aghamohammadi, M. R., Heidary, J. & Khooban, M. H. (2024). Watermarking-Based Defense Mechanism in LFC of Electricity Grids Compromised by Covert Attacks. IEEE Transactions on Circuits and Systems II: Express Briefs, 71(10), 4576-4580. https://doi.org/10.1109/TCSII.2024.3396734
Oshnoei, S., Aghamohammadi, M. R. & Khooban, M. H. (2024). Smart Frequency Control of Cyber-Physical Power System Under False Data Injection Attacks. IEEE Transactions on Circuits and Systems I: Regular Papers, 71(12), 5582-5595. https://doi.org/10.1109/TCSI.2024.3396703
Oshnoei, S., Heidari, J., Mahboubi-Moghaddam, E., Gheisarnejad, M. & Khooban, M. H. (2025). Identification and Mitigation of Data Integrity Stealth Attacks in Frequency Regulation of Power Systems. IEEE Transactions on Information Forensics and Security, 20, 6133-6148. https://doi.org/10.1109/TIFS.2025.3578922
Oshnoei, S., Peykarporsan, R., Heidari, J., Mahboubi-Moghaddam, E., Lie, T. T. & Khooban, M. H. (2026). A Novel Model-Free Defense Scheme for Power Systems Stability Under Cyber Attacks. IET Generation, Transmission and Distribution, 20(1), Article e70218. https://doi.org/10.1049/gtd2.70218
Oshnoei, S., Hassani, S., Gheisarnejad, M. & Mahboubi-Moghaddam, E. (2026). Real-Time Evaluation of Cyberattack-Resilient Control for Secure Large-Scale Power Networks. IEEE Internet of Things Journal, 13(3), 4449-4462. https://doi.org/10.1109/JIOT.2025.3633503
Oshnoei, S., Mahboubi-Moghaddam, E. & Khooban, M. H. (2025). Advanced Defense Strategy for Cyber-Resilient Frequency Control in Real Power Grids. IEEE Transactions on Automation Science and Engineering, 22, 19366-19376. https://doi.org/10.1109/TASE.2025.3594891
Oshnoei, S., Gheisarnejad, M., Sharifzadeh, M., Laurendeau, E. & Al-Haddad, K. (2025). Securing Grid-Connected Packed E-Cell Multilevel Inverter: A LSTM-AE Approach to Hybrid Attack Mitigation. IEEE Transactions on Automation Science and Engineering, 22, 21853-21863. https://doi.org/10.1109/TASE.2025.3614757
Oshnoei, S., Gheisarnejad, M., Fathollahi, A., Sharifzadeh, M., Laurendeau, E. & Al-Haddad, K. (2025). Resilient Cyber-Attack Detection and Mitigation in Grid-Tied PEC9 Inverter Using A3C-Based Adaptive Control. In 2025 IEEE Energy Conversion Conference Congress and Exposition, ECCE 2025 IEEE. https://doi.org/10.1109/ECCE58356.2025.11259768
Ong, S.-Q., Pinoy, N., Hui Lin, M., Bjerge, K., Peris-Felipo, F. J., Lind, R., P. Cuff, J., M. Cook, S. & Høye, T. T. (2025). ScannerVision: Scanner-based image acquisition of medically important arthropods for the development of computer vision and deep learning models. Current Research in Parasitology & Vector-Borne Diseases, 7, Article 100268. https://doi.org/10.1016/j.crpvbd.2025.100268
Ong, S. Q., Lim, M. H., Bjerge, K., Peris-Felipo, F. J., Lind, R. & Høye, T. T. (2026). InsectRoleVision: A computer vision system to study arthropod diversity and functional roles. Ecological Informatics, 95, Article 103701. https://doi.org/10.1016/j.ecoinf.2026.103701
Olsen, M. L. S., Thorlund, J. B., Zachariae, R., Vach, W., Bendix, L., Jespersen, J., Kidmose, P., Parsons, C., Kidholm, K. & Vægter, H. B. (2025). Digitally delivered cognitive behavioral therapy for insomnia (CBT-I) for patients with chronic pain and insomnia (The Back2Sleep Trial): study protocol for a randomized controlled trial. Current Controlled Trials in Cardiovascular Medicine, 26(1), Article 292. https://doi.org/10.1186/s13063-025-09013-3
Olsen, P. J., Tind Damgaard, A., Banluesombatkul, N. & Mikkelsen, K. B. (2025). EEG data alignment across devices using a neural network. In 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2025 - Proceedings IEEE. https://doi.org/10.1109/EMBC58623.2025.11254069
Oleksiienko, I. & Iosifidis, A. (2021). Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems. In N. Mallenahalli, A. Bhattacharya, S. Senatore, A. Negi & A. Hirose (Eds.), 2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) (pp. 59-64). IEEE. https://doi.org/10.1109/ICETCI51973.2021.9574075
Oleksiienko, I., Tran, D. T. & Iosifidis, A. (2022). Variational Neural Networks implementation in Pytorch and JAX[Formula presented]. Software Impacts, 14, Article 100431. https://doi.org/10.1016/j.simpa.2022.100431
Oleksiienko, I. & Iosifidis, A. (2022). 3D object detection and tracking. In A. Iosifidis & A. Tefa (Eds.), Deep Learning for Robot Perception and Cognition (pp. 313-340). Elsevier. https://doi.org/10.1016/B978-0-32-385787-1.00018-X
Oleksiienko, I., Nousi, P., Passalis, N., Tefas, A. & Iosifidis, A. (2023). Variational Voxel Pseudo Image Tracking. In 2023 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 323-328). IEEE. https://doi.org/10.1109/SSCI52147.2023.10371810
Oleksiienko, I. & Iosifidis, A. (2023). Layer Ensembles. In D. Comminiello & M. Scarpiniti (Eds.), 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP) IEEE. https://doi.org/10.1109/MLSP55844.2023.10286005
Oleksiienko, I., Tran, D. T. & Iosifidis, A. (2023). Variational Neural Networks. Procedia Computer Science, 222, 104-113. https://doi.org/10.1016/j.procs.2023.08.148

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