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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Heidari, N. & Iosifidis, A. (2021). Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout for Landmark-based Facial Expression Recognition with Uncertainty Estimation. arxiv.org. https://doi.org/10.48550/arXiv.2106.04332

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

Kiranyaz, S., Ince, T., Iosifidis, A. & Gabbouj, M. (2016). Progressive Operational Perceptrons. Neurocomputing, 224, 142-154. https://doi.org/10.1016/j.neucom.2016.10.044

Heidari, N. & Iosifidis, A. (2021). Progressive Graph Convolutional Networks for Semi-Supervised Node Classification. IEEE Access, 9, 81957-81968. https://doi.org/10.1109/ACCESS.2021.3085905

Tran, D. T., Gabbouj, M. & Iosifidis, A. (2022). Progressive and compressive learning. In A. Iosifidis & A. Tefas (Eds.), Deep Learning for Robot Perception and Cognition (pp. 187-220). Elsevier. https://doi.org/10.1016/B978-0-32-385787-1.00014-2

Kakavandi, F., de Reus, R., Gomes, C., Heidari, N., Iosifidis, A. & Larsen, P. G. (2022). Product Quality Control in Assembly Machine under Data Restricted Settings. In 2022 IEEE 20th International Conference on Industrial Informatics, INDIN 2022 (pp. 735-741). IEEE. https://doi.org/10.1109/INDIN51773.2022.9976173

Madsen, S. L., Karstoft, H. & Nyholm Jørgensen, R. (2018). Producing Artificial Data Samples Using Generative Adversarial Networks.. Poster session presented at 2018 summer school on Optimization in Image Analysis, Sønderborg, Denmark. http://optimization-image-analysis.compute.dtu.dk/posters/simon_madsen.pdf

Naveed, K., Madsen, L. M., Grombacher, D. & Larsen, J. J. (2024). Processing Time-domain Induced Polarization Data Using Variational Mode Decomposition. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-13. Article 5905713. https://doi.org/10.1109/TGRS.2024.3362290

Larsen, J. J. & Behroozmand, A. A. (2016). Processing of surface-nuclear magnetic resonance data from sites with high noise levels. Geophysics, 81(4), WB75-WB83. https://doi.org/10.1190/geo2015-0441.1

Larsen, J. J. & Behroozmand, A. A. (2015). Processing of Surface-NMR Data From Sites With High Noise. Abstract from AGU Fall Meeting 2015, San Fransisco, United States. https://agu.confex.com/agu/fm15/meetingapp.cgi/Paper/69401

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

Jørgensen, R., Nørremark, M., Sørensen, C. A. G. & Andersen, N. A. (2004). Process and behaviour based scripting language for weeding agents. In CIGR2004, Beijing, China. Paper on CD: ISBN 7-80167-707-2/ (pp. 537) https://www.researchgate.net/publication/280315713_Process_and_behaviour_based_scripting_language_for_weeding_agents

Dalsgaard, P., Pedersen, C. F. & Andersen, O. (Eds.) (2008). Proceedings for ISCA ITRW Speech Analysis and Processing for Knowledge Discovery.

Aytekin, C., Iosifidis, A. & Gabbouj, M. (2018). Probabilistic Saliency Estimation. Pattern Recognition, 74, 359-372. https://doi.org/10.1016/j.patcog.2017.09.023

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. Article 8298533. https://doi.org/10.1109/TSG.2018.2807845

Laakom, F., Raitoharju, J., Iosifidis, A., Tuna, U., Nikkanen, J. & Gabbouj, M. (2020). Probabilistic Color Constancy. In 2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings (pp. 978-982). Article 9190893 IEEE. https://doi.org/10.1109/ICIP40778.2020.9190893

Iosifidis, A. (2020). Probabilistic Class-Specific Discriminant Analysis. IEEE Access, 8, 183847-183855. https://doi.org/10.1109/ACCESS.2020.3029514

Christiansen, M. P., Teimouri, N., Laursen, M. S., Mikkelsen, B. F., Jorgensen, R. N. & Sorensen, C. A. G. (2019). Preprocessed sentinel-1 data via a web service focused on agricultural field monitoring. IEEE Access, 7(1), 65139-65149. Article 8715769. https://doi.org/10.1109/ACCESS.2019.2917063

Mortensen, A. K., Karstoft, H., Søegaard, K., Gislum, R. & Nyholm Jørgensen, R. (2017). Preliminary Results of Clover and Grass Coverage and Total Dry Matter Estimation in Clover-Grass Crops Using Image Analysis. Journal of Imaging, 3(4), Article 59. https://doi.org/10.3390/jimaging3040059

Markopoulos, A., Dalsgaard, P., Gkanas, I., Jensen, J. J., Jiang, B., Kaldanis, V., Larsen, L. B., Pedersen, C. F., Christensen, D. S., Schultz, N., Sørensen, L. T. & Peréz Vila, J. (2004). Preliminary report: Draft user centric scenarios for PNs of a valid architecture. Information Society Technologies, My Personal Adaptive Global NET (MAGNET), IST 507102.

Baltakys, K., Baltakienė, M., Heidari, N., Iosifidis, A. & Kanniainen, J. (2023). Predicting the trading behavior of socially connected investors: Graph neural network approach with implications to market surveillance. Expert Systems with Applications, 228, Article 120285. https://doi.org/10.1016/j.eswa.2023.120285

Shabani, M., Magris, M., Tzagkarakis, G., Kanniainen, J. & Iosifidis, A. (2023). Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots. Neural Computing and Applications, 35(25), 18519–18531. https://doi.org/10.1007/s00521-023-08674-y

Lillelund, C. M., Pannullo, F., Jakobsen, M. O. & Pedersen, C. F. (2023). Predicting Survival Time of Ball Bearings in the Presence of Censoring. In AAAI Symposium on Survival Prediction: Algorithms, Challenges, and Applications (SPACA) AAAI Press.

Kragh, M. F., Rimestad, J., Lassen, J. T., Berntsen, J. & Karstoft, H. (2022). Predicting embryo viability based on self-supervised alignment of time-lapse videos. IEEE Transactions on Medical Imaging, 41(2), 465-475. https://doi.org/10.1109/TMI.2021.3116986

Skovsen, S., Dyrmann, M., Eriksen, J., Gislum, R., Karstoft, H. & Nyholm Jørgensen, R. (2018). Predicting Dry Matter Composition of Grass Clover Leys Using Data Simulation and Camera-based Segmentation of Field Canopies into White Clover, Red Clover, Grass and Weeds. In Proceedings of the 14th International Conference on Precision Engineering Article 5079 International Society of Precision Agriculture. https://ispag.org/proceedings/?action=abstract&id=5079

Skovsen, S., Dyrmann, M., Eriksen, J., Gislum, R., Karstoft, H. & Nyholm Jørgensen, R. (2018). Predicting Dry Matter Composition of Grass Clover Leys Using Data Simulation and Camera-based Segmentation of Field Canopies into White Clover, Red Clover, Grass and Weeds. Abstract from International Conference on Precision Agriculture, Montréal, Quebec, Canada. https://ispag.org/proceedings/?action=abstract&id=5079&search=authors

D’Ambrosio, F., Harbo, M., Contiero, D., Bonfigli, A. R., Cicconi, D., Heuer, N., Roos, A., Pedersen, C. F., Fabbietti, P. & Gagliardi, C. (2024). Preact to lower the risk of falling by customized rehabilitation across Europe: the feasibility study protocol of the PRECISE project in Italy. Frontiers in Public Health, 12, Article 1293621. https://doi.org/10.3389/fpubh.2024.1293621

Jørgensen, R. & Sørensen, C. A. G. (2005). Præcisionsjordbrug - hvor svært kan det være? In Proc. Plantekongres 2005, Herning Kongrescenter. ISBN 87-984996-6-1 (pp. 74-75) https://www.researchgate.net/publication/280306170_Praecisionsjordbrug_-_hvor_svaert_kan_det_vaere

Grombacher, D., Liu, L., Larsen, J. J. & Auken, E. (2018). Practical considerations for small receive coils in surface NMR. Journal of Applied Geophysics, 154, 81-92. https://doi.org/10.1016/j.jappgeo.2018.04.005

Li, L., Looney, D., Park, C., Rehman, N. U. & Mandic, D. P. (2011). Power independent EMG based gesture recognition for robotics. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE. https://doi.org/10.1109/iembs.2011.6090036

Grombacher, D. J., Larsen, J. J. & Auken, E. (2016). Potential for Square Wave Transmitters in Surface NMR. In Near Surface Geoscience 2016: 22nd European Meeting of Environmental and Engineering Geophysics (Vol. 2016). European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.201602008

Christiansen, P., Kragh, M. F., Steen, K. A., Karstoft, H. & Nyholm Jørgensen, R. (2017). Platform for evaluating sensors and human detection in autonomous mowing operations. Precision Agriculture, 18(3), 350-365. https://doi.org/10.1007/s11119-017-9497-6

Dyrmann, M., Karstoft, H. & Midtiby, H. S. (2016). Plant species classification using deep convolutional neural network. Biosystems Engineering, 151(November), 72-80. https://doi.org/10.1016/j.biosystemseng.2016.08.024

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

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). Article 8815352 IEEE.

Dyrmann, M., Mortensen, A. K., Midtiby, H. S. & Nyholm Jørgensen, R. (2016). Pixel-wise classification of weeds and crops in images by using a Fully Convolutional neural network. Paper presented at International Conference on Agricultural Engineering 2016, Aarhus, Denmark. http://conferences.au.dk/uploads/tx_powermail/cigr2016paper_semanticsegmentation.pdf

Gislum, R. & Nyholm Jørgensen, R. (2016). Pilotprojekt vedr. billedanalyse på golfbaner med henblik på reduktion i pesticidforbruget og implementering af IPM.

Jun Hao Nicholas, N., Bahadir Kocer, B., Kayacan, E. & Wai Yee, Y. (2015). Piezoceramic Materials for Energy Harvesting on 3D Printed Unmanned Aerial Vehicles: A Feasibility Study. In Humanitarian Technology Conference (R10-HTC), 2015 IEEE Region 10 (pp. 1-4). IEEE. https://doi.org/10.1109/R10-HTC.2015.7391845

Zehtabiyan-Rezaie, N., Iosifidis, A. & Abkar, M. (2023). Physics-Guided Machine Learning for Wind-Farm Power Prediction: Toward Interpretability and Generalizability. PRX Energy, 2(1), Article 013009. https://doi.org/10.1103/PRXEnergy.2.013009

Iosifidis, A., Tefas, A. & Pitas, I. (2011). Person Specific Activity Recognition using Fuzzy Learning and Discriminant Analysis. In Signal Processing Conference, 2011 19th European (pp. 1974-1978). IEEE. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7074111

Iosifidis, A., Tefas, A. & Pitas, I. (2013). Person Identification from Actions based on Dynemes and Discriminant Learning. In Biometrics and Forensics (IWBF), 2013 International Workshop on IEEE. https://doi.org/10.1109/IWBF.2013.6547320

Iosifidis, A., Tefas, A. & Pitas, I. (2013). Person Identification from Actions based on Artificial Neural Networks. In Computational Intelligence in Biometrics and Identity Management (CIBIM), 2013 IEEE Workshop on (pp. 7-13). IEEE. https://doi.org/10.1109/CIBIM.2013.6607907

Ivasic-Kos, M., Iosifidis, A., Tefas, A. & Pitas, I. (2014). Person De-identification in Activity Videos. In Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014 37th International Convention on (pp. 1294-1299). IEEE. https://doi.org/10.1109/MIPRO.2014.6859767

Ma, X., J. Hegarty, P., Abildgaard Pedersen, J., Johansen, L. G. & Larsen, J. J. (2016). Personal Sound Zones: The significance of loudspeaker driver nonlinear distortion. Paper presented at 2016 AES International Conference on Sound Field Control, Guildford, United Kingdom. https://secure.aes.org/forum/pubs/conferences/?elib=18305

Tran, D. T., Gabbouj, M. & Iosifidis, A. (2020). Performance Indicator in Multilinear Compressive Learning. In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 1822-1828). Article 9308418 IEEE. https://doi.org/10.1109/SSCI47803.2020.9308418

Maslim, R., Chaoyi, H., Yixi, Z., Linhao, J., Bahadir Kocer, B. & Kayacan, E. (2015). Performance evaluation of adaptive and nonadaptive fuzzy structures for 4D trajectory tracking of quadrotors: A comparative study. In Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on (pp. 1-7). IEEE. https://doi.org/10.1109/FUZZ-IEEE.2015.7337945

Ferrarini, B., Ehsan, S., Leonardis, A., Rehman, N. U. & McDonald-Maier, K. D. (2018). Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database. IEEE Access, 6, 8564-8573. https://doi.org/10.1109/access.2018.2795460

Angelov, P., Bernardi, M. L., Nardini, F. M., Pecori, R., Valerio, L., Dini, P., Ashraf, S., Aversano, L., Bianchini, M., Brutti, A., Bukovsky, I., Cagnoni, S., Cao, J., Cimitile, M., Dazzi, P., Iammarino, M., Iosifidis, A., Michahelles, F., Mora, A. ... Welsh, M. (2024). PerconAI 2024: 3rd Workshop on Pervasive and Resource-Constrained Artificial Intelligence - Welcome and Committees. In 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (pp. 34-35). IEEE. https://doi.org/10.1109/PerComWorkshops59983.2024.10503466

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

Griffiths, M. P., Grombacher, D. & Larsen, J. J. (2020). Parallelized Hybrid Bloch Solver for Surface Nuclear Magnetic Resonance. Abstract from Near Surface Geoscience 2020.

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