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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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 University, DCE - Danish Centre for Environment and Energy. Videnskabelig rapport fra DCE - Nationalt Center for Miljø og Energi Nr. 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. I O. Huguenin-Elie, B. Studer, R. Kölliker, D. Reheul, M. Probo, P. Barre, U. Feuerstein, I. Roldan-Ruiz, P. Mariotte & A. Hopkins (red.), 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 (s. 103-109). Wageningen Academic Publishers.

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

Iosifidis, A. (2020). Danish researchers develop artificial intelligence to expose stock-market fraud. I J. Bruun (red.), Profile 2020 (s. 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, Artikel 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. Artikel 8976275. https://doi.org/10.1109/TTE.2020.2970674

Nyholm Jørgensen, R. (2020). Plantekongres 2020 - RoboWeedMaps præsenterer: Flere muligheder med digital ukrudtsgenkendelse. Billeder, Video- og Lydoptagelser (digital) https://www.youtube.com/watch?v=HEsJ9k1SJYM

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, Artikel 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, Artikel 103869. https://doi.org/10.1016/j.jappgeo.2019.103869

Asif, M. R., Qi, C., Wang, T., Fareed, M. S. & Khan, S. (2019). License plate detection for multi-national vehicles – a generalized approach. Multimedia Tools and Applications, 78(24), 35585-35606. https://doi.org/10.1007/s11042-019-08199-4

Panagiotatos, G., Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2019). Curriculum-based teacher ensemble for robust neural network distillation. I 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. I 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. I 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings (s. 854-858). Artikel 8802945 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. I 2019 IEEE International Conference on Image Processing (ICIP) (s. 1168-1172). Artikel 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. I 27th European Signal Processing Conference EUSIPCO 2019 IEEE. 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. I 27th European Signal Processing Conference EUSIPCO 2019 IEEE. 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. I 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019: Proceedings Artikel 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. (s. 1085-1090). 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), 774-785. 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. Artikel 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 . Artikel 8713851. 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. Artikel 8743410. 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. Artikel 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

Hansen, O. L. P., Svenning, J.-C., Olsen, K., Dupont, S., Garner, B. H., Iosifidis, A., Price, B. W. & Høye, T. T. (2019). Image data used for publication "Species-level image classification with convolutional neural network enable insect identification from habitus images". Datasæt https://doi.org/10.5281/zenodo.3549369

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. I 2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019 Artikel 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. I Proceedings of the American Control Conference: 2019 American Control Conference, ACC 2019 (s. 279-285). Artikel 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), Artikel 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

Nyholm Jørgensen, R., Laursen, M. S., Teimouri, N., Madsen, S. L., Dyrmann, M., Somerville, G. J. & Mathiassen, S. K. (2019). RoboWeedMaps - Automated weed detection and mapping - Invited talk at SSWM 2019, SDU, Odense Denmark. Billeder, Video- og Lydoptagelser (digital), YouTube.

Nyholm Jørgensen, R. (2019). RoboWeedMaps - Walking the weed maps with your mobile phone. Billeder, Video- og Lydoptagelser (digital), YouTube. https://www.youtube.com/watch?v=9rKahKe_a5c&t=108s

Kragh, M. F., Rimestad, J., Berntsen, J. & Karstoft, H. (2019). Automatic grading of human blastocysts from time-lapse imaging. Computers in Biology and Medicine, 115, Artikel 103494. https://doi.org/10.1016/j.compbiomed.2019.103494

Mehndiratta, M. & Kayacan, E. (2019). A constrained instantaneous learning approach for aerial package delivery robots: onboard implementation and experimental results. Autonomous Robots, 43(8), 2209-2228. https://doi.org/10.1007/s10514-019-09875-y

Nyholm Jørgensen, R. (2019). RoboWeedMaps præsenterer pletsprøjtning på FutureCropping demodag den 9 oktober 2019. Billeder, Video- og Lydoptagelser (digital), YouTube. https://www.youtube.com/watch?v=YKCR-hARLS8&feature=youtu.be

Sarabakha, A. & Kayacan, E. (2019). Online deep learning for improved trajectory tracking of unmanned aerial vehicles using expert knowledge. I 2019 International Conference on Robotics and Automation, ICRA 2019 (s. 7727-7733). Artikel 8794314 IEEE. https://doi.org/10.1109/ICRA.2019.8794314

Imanberdiyev, N., Monica, J. & Kayacan, E. (2019). A multi-task velocity-based redundancy resolution strategy for unmanned aerial manipulators. I 2019 18th European Control Conference, ECC 2019 (s. 1130-1135). Artikel 8796272 IEEE. https://doi.org/10.23919/ECC.2019.8796272

Zhou, S., Helwa, M. K., Schoellig, A. P., Sarabakha, A. & Kayacan, E. (2019). Knowledge transfer between robots with similar dynamics for high-accuracy impromptu trajectory tracking. I 2019 18th European Control Conference, ECC 2019 Artikel 8796140 IEEE. https://doi.org/10.23919/ECC.2019.8796140

Govdeli, Y., Moheed Bin Muzaffar, S., Raj, R., Elhadidi, B. & Kayacan, E. (2019). Unsteady aerodynamic modeling and control of pusher and tilt-rotor quadplane configurations. Aerospace Science and Technology, 94, Artikel 105421. https://doi.org/10.1016/j.ast.2019.105421

Tran, D. T., Kiranyaz, S., Gabbouj, M. & Iosifidis, A. (2019). PyGOP: A Python library for Generalized Operational Perceptron algorithms. Knowledge-Based Systems, 182, Artikel 104801. https://doi.org/10.1016/j.knosys.2019.06.009

Madsen, S. L., Dyrmann, M., Nyholm Jørgensen, R. & Karstoft, H. (2019). Generating artificial images of plant seedlings using generative adversarial networks. Biosystems Engineering, 187, 147-159. https://doi.org/10.1016/j.biosystemseng.2019.09.005

Gautam, C., Tiwari, A. & Iosifidis, A. (2019). Minimum Variance-Embedded Multi-layer Kernel Ridge Regression for One-class Classification. I S. Sundaram (red.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 (s. 389-396). Artikel 8628692 IEEE. https://doi.org/10.1109/SSCI.2018.8628692

Liu, L., Grombacher, D., Auken, E. & Juul Larsen, J. (2019). Apsu: A wireless multichannel receiver system for surface nuclear magnetic resonance groundwater investigations. Geoscientific Instrumentation, Methods and Data Systems, 8(1), 1-11. https://doi.org/10.5194/gi-8-1-2019

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Revideret 10.08.2023

AU Engineering