Publications by Signal Processing & Machine Learning

Are you looking for publications by Section of Signal Processing & Machine Learning? On this page you can find all the publications made by the Section of Signal Processing & Machine Learning - Department of Electrical and Computer Engineering, Aarhus University.

Below you can find a list of all the publications, their publishing date, their author(s), and titles. The list can be sorted by date, author, and title:

List of Publications

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Ärje, J., Raitoharju, J., Iosifidis, A., Tirronen, V., Meissner, K., Gabbouj, M., Kiranyaz, S. & Kärkkäinen, S. (2020). Human experts vs. machines in taxa recognition. Signal Processing: Image Communication, 87, [115917]. https://doi.org/10.1016/j.image.2020.115917

Ntakaris, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2020). Mid-price prediction based on machine learning methods with technical and quantitative indicators. PLOS ONE, 15(6), [e0234107]. https://doi.org/10.1371/journal.pone.0234107

Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2020). Temporal logistic neural Bag-of-Features for financial time series forecasting leveraging limit order book data. Pattern Recognition Letters, 136, 183-189. https://doi.org/10.1016/j.patrec.2020.06.006

Rafiei Foroushani, M., Niknam, T., Aghaei, J., Shafie-khah, M. & P.S.Catalão, J. (2018). Probabilistic Load Forecasting Using an Improved Wavelet Neural Network Trained by Generalized Extreme Learning Machine. IEEE Transactions on Smart Grid, 9(6), 6961 - 6971. https://doi.org/10.1109/TSG.2018.2807845

Farsizadeh, H., Gheisarnejad, M., Mosayebi, M., Rafiei Foroushani, M. & Khooban, M. H. (2020). An Intelligent and Fast Controller for DC/DC Converter Feeding CPL in a DC Microgrid. IEEE Transactions on Circuits and Systems II: Express Briefs, 67(6), 1104-1108. https://doi.org/10.1109/TCSII.2019.2928814

Hasanvand , S., Rafiei Foroushani, M., Gheisarnejad, M. & Khooban, M. H. (2020). Reliable Power Scheduling of an Emission-Free Ship: Multi-Objective Deep Reinforcement Learning. IEEE Transactions on Transportation Electrification, 6(2), 832-843. https://doi.org/10.1109/TTE.2020.2983247

Bonatti, R., Wang, W., Ho, C., Ahuja, A., Gschwindt, M., Camci, E., Kayacan, E., Choudhury, S. & Scherer, S. (2020). Autonomous aerial cinematography in unstructured environments with learned artistic decision-making. Journal of Field Robotics, 37(4), 606-641. https://doi.org/10.1002/rob.21931

Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2020). Using Deep Learning for price prediction by exploiting stationary limit order book features. Applied Soft Computing Journal, 93, [106401]. https://doi.org/10.1016/j.asoc.2020.106401

Farkhani, S., Kragh, M. F., Christiansen, P. H., Nyholm Jørgensen, R. & Karstoft, H. (2019). Sparse-to-Dense Depth Completion in Precision Farming. In ICVISP 2019: Proceedings of the 3rd International Conference on Vision, Image and Signal Processing [35] Association for Computing Machinery. https://doi.org/10.1145/3387168.3387230

Krestenitis, M., Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2020). Recurrent bag-of-features for visual information analysis. Pattern Recognition, 106, [107380]. https://doi.org/10.1016/j.patcog.2020.107380

Mehndiratta, M., Kayacan, E., Reyhanoglu, M. & Kayacan, E. (2020). Robust Tracking Control of Aerial Robots Via a Simple Learning Strategy-Based Feedback Linearization. IEEE Access, 8, 1653-1669. https://doi.org/10.1109/ACCESS.2019.2962512

Kiranyaz, S., Ince, T., Iosifidis, A. & Gabbouj, M. (2020). Operational neural networks. Neural Computing and Applications, 32(11), 6645-6668. https://doi.org/10.1007/s00521-020-04780-3

Letafat, A., Rafiei, M., Ardeshiri, M., Sheikh, M., Banaei, M., Boudjadar, J. & Khooban, M. H. (2020). An Efficient and Cost-Effective Power Scheduling in Zero-Emission Ferry Ships. Complexity, 2020, [6487873]. https://doi.org/10.1155/2020/6487873

Rafiei Foroushani, M., Boudjadar, J. & Khooban, M. H. (2021). Energy Management of a Zero-Emission Ferry Boat with a Fuel Cell-Based Hybrid Energy System: Feasibility Assessment. IEEE Transactions on Industrial Electronics, 68(2), 1739-1748. https://doi.org/10.1109/TIE.2020.2992005

Madsen, S. L. (2020). Advancing visual recognition of plant seedlings through generative modelling and data collection. Aarhus Universitet.

Madsen, S. L., Mathiassen, S. K., Dyrmann, M., Laursen, M. S., Paz, L-C. & Nyholm Jørgensen, R. (2020). Open Plant Phenotype Database of Common Weeds in Denmark. Remote Sensing, 12(8), [1246]. https://doi.org/10.3390/rs12081246

Bjerge, K., Nielsen, J. B., Sepstrup, M. V., Helsing-Nielsen, F. & Høye, T. T. (2020). A light trap and computer vision system to detect and classify live moths(Lepidoptera) using tracking and deep learning. Bioarxiv

Høye, T. T., Mann, H. M. R. & Bjerge, K. (2020). Kamerabaseret overvågning af Insekter på grønne bytage. Aarhus Universitet, DCE – Nationalt Center for Miljø og Energi ©. Videnskabelig rapport fra DCE - Nationalt Center for Miljø og Energi No. 371 https://dce2.au.dk/pub/SR371.pdf

Grombacher, D., Kass, M. A., Auken, E. & Larsen, J. J. (2020). An approximate fast-mapping approach to the surface NMR forward problem. Geophysical Journal International, 221(2), 928-937. https://doi.org/10.1093/gji/ggaa044

Hansen, O. L. P., Svenning, J. C., Olsen, K., Dupont, S., Garner, B. H., Iosifidis, A., Price, B. W. & Høye, T. T. (2020). Species-level image classification with convolutional neural network enables insect identification from habitus images. Ecology and Evolution, 10(2), 737-747. https://doi.org/10.1002/ece3.5921

Patel, S., Sarabakha, A., Kircali, D. & Kayacan, E. (2020). An Intelligent Hybrid Artificial Neural Network-Based Approach for Control of Aerial Robots. Journal of Intelligent and Robotic Systems: Theory and Applications, 97(2), 387-398. https://doi.org/10.1007/s10846-019-01031-z

Eriksen, J., Frandsen, T. S., Knudsen, L., Skovsen, S., Nyholm Jørgensen, R., Steen, K. A., Green, O. & Rasmussen, J. (2019). Nitrogen fertilization of grass-clover leys. In O. Huguenin-Elie, B. Studer, R. Kölliker, D. Reheul, M. Probo, P. Barre, U. Feuerstein, I. Roldan-Ruiz, P. Mariotte & A. Hopkins (Eds.), Improving sown grasslands through breeding and management: Proceedings of the Joint 20th Symposium of the European Grassland Federation and the 33rd Meeting of the EUCARPIA Section "Fodder Crops and Amenity Grasses", Zürich, Switzerland, 24-27 June 2019 (pp. 103-109). Wageningen Academic Publishers. Grassland Science in Europe Vol. 24

Iosifidis, A. & Tefas, A. (2020). Deep Learning for Visual Content Analysis. Signal Processing: Image Communication, 83(April), [115806]. https://doi.org/10.1016/j.image.2020.115806

Iosifidis, A. (2020). Danish researchers develop artificial intelligence to expose stock-market fraud. In J. Bruun (Ed.), Profile 2020 (pp. 94-95). Aarhus Universitet, Institut for Ingeniørvidenskab . https://www.e-pages.dk/aarhusuniversitet/2231/html5/

Letafat, A., Rafiei Foroushani, M., Sheikh, M., Afshari-Igder, M., Banaei, M., Boudjadar, J. & Khooban, M. H. (2020). Simultaneous energy management and optimal components sizing of a zero-emission ferry boat. Journal of Energy Storage, 28, [101215]. https://doi.org/10.1016/j.est.2020.101215

Banaei, M., Rafiei Foroushani, M., Boudjadar, J. & Khooban, M. H. (2020). A Comparative Analysis of Optimal Operation Scenarios in Hybrid Emission-Free Ferry Ships. IEEE Transactions on Transportation Electrification, 6(1), 318-333. [8976275]. https://doi.org/10.1109/TTE.2020.2970674

Sørensen, R. A., Nielsen, M. & Karstoft, H. (2020). Routing in congested baggage handling systems using deep reinforcement learning. Integrated Computer-Aided Engineering, 27(2), 139-152. https://doi.org/10.3233/ICA-190613

Osterman, G., Haldrup, J., Larsen, J. J., Auken, E. & Grombacher, D. (2020). A cautionary tale: How phase compensation during surface nuclear magnetic resonance inversion conceals forward modelling errors. Journal of Applied Geophysics, 173, [103905]. https://doi.org/10.1016/j.jappgeo.2019.103905

Grombacher, D., Liu, L., Kass, M. A., Osterman, G., Fiandaca, G., Auken, E. & Larsen, J. J. (2020). Inverting surface NMR free induction decay data in a voltage-time data space. Journal of Applied Geophysics, 172, [103869]. https://doi.org/10.1016/j.jappgeo.2019.103869

Panagiotatos, G., Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2019). Curriculum-based teacher ensemble for robust neural network distillation. In 2019 27th European Signal Processing Conference (EUSIPCO) IEEE. https://doi.org/10.23919/EUSIPCO.2019.8903112

Thanh Tran, D., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2019). Data-driven Neural Architecture Learning for Financial Time-series Forecasting. In Digital Image and Signal Processing https://arxiv.org/ftp/arxiv/papers/1903/1903.06751.pdf

Passalis, N., Tefas, A., Iosifidis, A. & Gabbouj, M. (2019). Class-based Variational Representation Learning for Robust Image Retrieval. In 2019 IEEE International Conference on Image Processing (ICIP) (pp. 854-858). IEEE. https://doi.org/10.1109/ICIP.2019.8802945

Thanh Tran, D., Kiranyaz, S., Gabbouj, M. & Iosifidis, A. (2019). Knowledge Transfer For Face Verification Using Heterogeneous Generalized Operational Perceptrons. In 2019 IEEE International Conference on Image Processing (ICIP) (pp. 1168-1172). [8804296] IEEE. https://doi.org/10.1109/ICIP.2019.8804296

Panagiotatos, G., Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2019). Curriculum-based Teacher Ensemble for Robust Neural Network Distilation. In 27th European Signal Processing Conference EUSIPCO 2019 IEEE. Proceedings of the European Signal Processing Conference http://eusipco2019.org/Proceedings/papertitles.html

Ärje, J., Milioris, D., Tran, D. T., Jepsen, J. U., Raitoharju, J., Gabbouj, M., Iosifidis, A. & Høye, T. T. (2019). Automatic Flower Detection and Classification System Uing a Light-Weight Convolutional Neural Network. In 27th European Signal Processing Conference EUSIPCO 2019 IEEE. Proceedings of the European Signal Processing Conference http://eusipco2019.org/Proceedings/papertitles.html

Tzogka, C., Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2019). Less is more: Deep Learning using subjective annotations for sentiment analysis from social media. In 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019: Proceedings [8918792] IEEE. https://doi.org/10.1109/MLSP.2019.8918792

Laakom, F., Raitoharju, J., Iosifidis, A., Nikkanen, J. & Gabbouj, M. (2019). Color Constancy Convolutional Autoencoder. https://arxiv.org/abs/1906.01340

Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2020). Temporal Bag-of-Features Learning for Predicting Mid Price Movements using High Frequency Limit Order Book Data. IEEE Transactions on Emerging Topics in Computational Intelligence, 4(6), 774-758. https://doi.org/10.1109/TETCI.2018.2872598

Thanh Tran, D., Kiranyaz, S., Gabbouj, M. & Iosifidis, A. (2020). Heterogeneous Multilayer Generalized Operational Perceptron. IEEE Transactions on Neural Networks and Learning Systems, 31(3), 710-724. [8727718]. https://doi.org/10.1109/TNNLS.2019.2914082

Nousi, P., Tsantekidis, A., Passalis, N., Ntakaris, A., Kanniainen, J., Tefas, A., Gabbouj, M. & Iosifidis, A. (2019). Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data. IEEE Access, 7, 64722 - 64736 . https://doi.org/10.1109/ACCESS.2019.2916793

Mäkinen, M., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2019). Forecasting of Jump Arrivals in Stock Prices: New Attention-based Network Architecture using Limit Order Book Data. arXiv.org. https://arxiv.org/pdf/1810.10845.pdf

Ntakaris, A., Mirone, G., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2019). Feature Engineering for Mid-Price Prediction with Deep Learning. IEEE Access, 7, 82390 - 82412. https://doi.org/10.1109/ACCESS.2019.2924353

Cao, G., Iosifidis, A., Gabbouj, M., Raghavan, V. & Gottumukkala, R. (2021). Deep Multi-view Learning to Rank. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1426-1438. [8845659]. https://doi.org/10.1109/TKDE.2019.2942590

Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2019). Deep Adaptive Input Normalization for Time Series Forecasting. arXiv.org. https://arxiv.org/pdf/1902.07892.pdf

Thanh Tran, D., Kiranyaz, S., Gabbouj, M. & Iosifidis, A. (2020). Progressive Operational Perceptrons with Memory. Neurocomputing, 379, 172-181. https://doi.org/10.1016/j.neucom.2019.10.079

Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2020). Variance-preserving Deep Metric Learning for Content-based Image Retrieval. Pattern Recognition Letters, 131, 8-14. https://doi.org/10.1016/j.patrec.2019.11.041

Govdeli, Y., Bin Muzaffar, S. M., Raj, R., Elhadidi, B. & Kayacan, E. (2019). Learning Control of Tandem-Wing Tilt-Rotor UAV with Unsteady Aerodynamic Model. In 2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019 [8859023] IEEE. https://doi.org/10.1109/FUZZ-IEEE.2019.8859023

Camci, E. & Kayacan, E. (2019). Planning swift maneuvers of quadcopter using motion primitives explored by reinforcement learning. In Proceedings of the American Control Conference: 2019 American Control Conference, ACC 2019 (pp. 279-285). [8815352] IEEE.

Madsen, S. L., Mortensen, A. K., Nyholm Jørgensen, R. & Karstoft, H. (2019). Disentangling Information in Artificial Images of Plant Seedlings Using Semi-Supervised GAN. Remote Sensing, 11(22), [2671]. https://doi.org/10.3390/rs11222671

Kremer, T., Juul Larsen, J. & Nguyen, F. (2019). Processing harmonic EM noise with multiple or unstable frequency content in surface NMR surveys. Geophysical Journal International, 219(2), 753-775. https://doi.org/10.1093/gji/ggz307

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AU Engineering