Publikationer fra instituttet

Under publikationsliste finder du en samlet liste over de publikationer, som er lavet af medarbejdere ved Institut for Elektro- og Computerteknologi for eksempel tidsskriftsartikler, bøger, rapporter m.v.

Instituttet udgiver også tekniske rapporter, som du kan læse mere om under Tekniske rapporter.

Under ph.d.-afhandlinger finder du instituttets publicerede ph.d.-afhandlinger.

Publikationsliste

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Rashidi, A., Zamani, M., Mondal, T., Hosseini, S., Laursen, K., Corbett, B. & Moradi, F. (2023). Ultrasonically Powered and Controlled Microsystem for Dual-Wavelength Optogenetics With a Multiload Regulation Scheme. IEEE Solid-State Circuits Letters, 6, 33-36. https://doi.org/10.1109/LSSC.2023.3239601

Ramakrishna, S., Li, F., Kijeńska-Gawrońska, E. & Agarwala, S. (2023). Where is science heading? The main challenges before today’s scientists? Drying Technology, 41(6), 812-816. https://doi.org/10.1080/07373937.2023.2205799

Rahimi, M., Momenzadeh, M., Akhbari, A. & Khooban, M. H. (2023). Performance enhancement of parallel-operated inverter-based virtual synchronous generators supplying active load. IET Electric Power Applications, 17(6), 835-854. https://doi.org/10.1049/elp2.12307

Rafiei Foroushani, M., Griffiths, M. P., Boudjadar, J. & Khooban, M. H. (2021). Deep Learning-Based Energy Management of an All-electric City Bus with Wireless Power Transfer. IEEE Access, 9, 43981-43990. Artikel 9380640. https://doi.org/10.1109/ACCESS.2021.3066300

Rafiei Foroushani, M., Raitoharju, J. & Iosifidis, A. (2023). Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey. IEEE Access, 11, 2445 - 2477. https://doi.org/10.1109/ACCESS.2023.3234187

Rafiei Foroushani, M. & Iosifidis, A. (2023). Class-Specific Variational Auto-Encoder for Content-Based Image Retrieval. I 2023 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/IJCNN54540.2023.10191068

Rafiei Foroushani, M., Tran, D. T. & Iosifidis, A. (2023). Recognition of Defective Mineral Wool Using Pruned ResNet Models. I H. Dorksen, S. Scanzio, J. Jasperneite, L. Wisniewski, K. F. Man, T. Sauter, L. Seno, H. Trsek & V. Vyatkin (red.), 2023 IEEE 21st International Conference on Industrial Informatics (INDIN) IEEE. https://doi.org/10.1109/INDIN51400.2023.10217993

Rafiei Foroushani, M. (2024). Advancing Computer Vision in Irregular Texture Product Analysis. Aarhus University.

Radmanesh, H., Maalandish, M., Pourjafar, S. & Khooban, M. H. (2023). High step-up MIMO DC–DC power converter control-based pole placement. International Journal of Circuit Theory and Applications, 51(3), 1313-1331. https://doi.org/10.1002/cta.3469

Pullens, J. W. M., Sørensen, C. A. G. & Olesen, J. E. (2021). Temperature-based prediction of harvest date in winter and spring cereals as a basis for assessing viability for growing cover crops. Field Crops Research, 264, Artikel 108085. https://doi.org/10.1016/j.fcr.2021.108085

Pradeepkumar, J., Anandakumar, M., Kugathasan, V., Lalitharatne, T. D., De Silva, A. C. & Kappel, S. L. (2021). Decoding of Hand Gestures from Electrocorticography with LSTM Based Deep Neural Network. I 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 (s. 420-423). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC46164.2021.9630958

Pradeepkumar, J., Anandakumar, M., Kugathasan, V., Suntharalingham, D., Kappel, S. L., De Silva, A. C. & Edussooriya, C. U. S. (2024). Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 32, 2893-2904. https://doi.org/10.1109/TNSRE.2024.3438610

Potapenko, I., Thiesson, B., Kristensen, M., Hajari, J. N., Ilginis, T., Fuchs, J., Hamann, S. & la Cour, M. (2022). Automated artificial intelligence-based system for clinical follow-up of patients with age-related macular degeneration. Acta Ophthalmologica, 100(8), 927-936. https://doi.org/10.1111/aos.15133

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, Artikel 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. I H. D. Macedo & K. Pierce (red.), Proceedings of the 20th International Overture Workshop (s. 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. Artikel 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), Artikel 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. Afhandling præsenteret på 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, Prag, Tjekkiet. https://sarabakha.info/files/papers/conference/IROS_2021.pdf

Pham, H. X., Ugurlu, H. I., Fevre, J. L., Bardakci, D. & Kayacan, E. (2022). Deep learning for vision-based navigation in autonomous drone racing. I A. Iosifidis & A. Tefas (red.), Deep Learning for Robot Perception and Cognition (s. 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. (2023). Efficient deep learning-based monocular 3D perception for agile robot navigation. Århus Universitet.

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. I ISR Europe 2023: 56th International Symposium on Robotics, in cooperation with Fraunhofer IPA September 26 – 27, 2023 in Stuttgart (s. 16-21). VDE Verlag GmbH. https://ieeexplore.ieee.org/document/10363048

Perić, V. S. & Vanfretti, L. (2016). Optimal PMU placement for power system ambient data-based mode estimation applications. I ISGT Europe 2016 - IEEE PES Innovative Smart Grid Technologies, Europe Artikel 7856239 IEEE Computer Society. https://doi.org/10.1109/ISGTEurope.2016.7856239

Perić, V. S., Bombois, X. & Vanfretti, L. (2017). Optimal multisine probing signal design for power system electromechanical mode estimation. I T. X. Bui & R. Sprague (red.), Proceedings of the 50th Annual Hawaii International Conference on System Sciences, HICSS 2017 (s. 3156-3164). IEEE Computer Society.

Perić, V. S., Sarić, A. T. & Grabež, D. I. (2012). Coordinated tuning of power system stabilizers based on Fourier Transform and neural networks. Electric Power Systems Research, 88, 78-88. https://doi.org/10.1016/j.epsr.2012.01.017

Perić, V. S., Bogodorova, T., Mete, A. N. & Vanfretti, L. (2015). Model order selection for probing-based power system mode estimation. I 2015 IEEE Power and Energy Conference at Illinois, PECI 2015 Artikel 7064883 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PECI.2015.7064883

Peric, V. S., Bombois, X. & Vanfretti, L. (2016). Optimal Signal Selection for Power System Ambient Mode Estimation Using a Prediction Error Criterion. IEEE Transactions on Power Systems, 31(4), 2621-2633. Artikel 7279213. https://doi.org/10.1109/TPWRS.2015.2477490

Peric, V. S. & Vanfretti, L. (2014). Power-system ambient-mode estimation considering spectral load properties. IEEE Transactions on Power Systems, 29(3), 1133-1143. Artikel 6683082. https://doi.org/10.1109/TPWRS.2013.2292331

Peric, V. S., Baudette, M., Vanfretti, L., Gjerde, J. O. & Lovlund, S. (2014). Implementation and testing of a real-time mode estimation algorithm using ambient PMU data. Afhandling præsenteret på 2014 Clemson University Power Systems Conference, PSC 2014, Clemson, SC, USA. https://doi.org/10.1109/PSC.2014.6808116

Peric, V. S., Hamacher, T., Mohapatra, A., Christiange, F., Zinsmeister, D., Tzscheutschler, P., Wagner, U., Aigner, C. & Witzmann, R. (2020). CoSES laboratory for combined energy systems at TU munich. I 2020 IEEE Power and Energy Society General Meeting, PESGM 2020 Artikel 9281442 IEEE Computer Society. https://doi.org/10.1109/PESGM41954.2020.9281442

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. I G. A. Papadopoulos, A. Achilleos, E. Pissaloux & R. Velázquez (red.), ICT for Health, Accessibility and Wellbeing - 2nd International Conference, IHAW 2022, Revised Selected Papers (s. 189-199). Springer. https://doi.org/10.1007/978-3-031-29548-5_13

Pauwels, R. & Iosifidis, A. (2024). Deep Learning in Image Processing: Part 1—Types of Neural Networks, Image Segmentation. I Artificial Intelligence in Dentistry (s. 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. I K. Orhan & R. Jagtap (red.), Artificial Intelligence in Dentistry (s. 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. I 2020 28th European Signal Processing Conference (EUSIPCO (s. 1392-1396). Artikel 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

Park, C., Looney, D., Rehman, N. U., Ahrabian, A. & Mandic, D. P. (2013). Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(1), 10-22. https://doi.org/10.1109/tnsre.2012.2229296

Paredis, R., Gonçalves Gomes, C. Â. & Vangheluwe, H. (2021). Towards a Family of Digital Model/Shadow/Twin Workflows and Architectures: I H. Panetto, M. Macchi & K. Madani (red.), Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2021 (s. 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. I A. Smirnov, H. Panetto & K. Madani (red.), 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 (s. 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), Artikel 110904. https://doi.org/10.1016/j.est.2024.110904

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), Artikel 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, Artikel 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

Oleksiienko, I. & Iosifidis, A. (2021). Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems. I N. Mallenahalli, A. Bhattacharya, S. Senatore, A. Negi & A. Hirose (red.), 2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) (s. 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, Artikel 100431. https://doi.org/10.1016/j.simpa.2022.100431

Oleksiienko, I. & Iosifidis, A. (2022). 3D object detection and tracking. I A. Iosifidis & A. Tefa (red.), Deep Learning for Robot Perception and Cognition (s. 313-340). Elsevier. https://doi.org/10.1016/B978-0-32-385787-1.00018-X

Oleksiienko, I. (2023). Uncertainty Estimation for 3D Object Detection and Tracking. Aarhus University.

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