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Department of Electrical and Computer Engineering

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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Jensen, L. A., Sørensen, C. A. G. & Jørgensen, R. (2007). Real-time internet-based traceability unit for mobile payload vehicles. In Proceedings of the XXXII CIOSTA-CIGR Section V Conference "Advances in Labour and Machinery Management for a Profitable Agriculture and Forestry" (pp. 368-374). <Forlag uden navn>.

Bjerge, K., Mann, H. M. R. & Høye, T. T. (2022). Real-time insect tracking and monitoring with computer vision and deep learning. Remote Sensing in Ecology and Conservation, 8(3), 315-327. Advance online publication. https://doi.org/10.1002/rse2.245

Laursen, M. S., Larsen, R., Jensen, K. & Nyholm Jørgensen, R. (2016). Real time back projection onto prerecorded DHM surface of UAV recorded images with RTK GPS and IMU. Paper presented at International Conference on Agricultural Engineering 2016, Aarhus, Denmark. http://conferences.au.dk/uploads/tx_powermail/2016cigr_real__time__back__projection__onto_prerecorded_dhm_surface_of_uav_recorded_images_with_rtk_gps_and_imu.pdf

Jørgensen, R., Sørensen, C. A. G., Bak, T. & Moore, K. (2004). Rational agents for agricultural crop surveying: Adaptive task and motion planning. In Proc. 5th int. workshop on Artificial Intelligence in Agriculture (AIA'2004), (Rafea, M., (ed.)), Cairo, Egypt (pp. 11-16)

Miranda, J., Cabral, J., Ravelo, B., Wagner, S. R., Pedersen, C. F., Memon, M. & Mathiesen, M. (2015). Radiated EMC immunity investigation of common recognition identification platform for medical applications. Journal of Physics D: Applied Physics, 69(1). https://doi.org/10.1051/epjap/2014140230

Ravelo, B., Miranda, J., Cabral, J., Wagner, S. R., Pedersen, C. F., Memon, M. & Mathiesen, M. (2016, Mar 24). Radiated EMC Characterization of Common Recognition and Identification Platform for E-Healthcare.

Yan, R. J., Kayacan, E., Chen, I. M., Tiong, L. K. & Wu, J. (2019). QuicaBot: Quality Inspection and Assessment Robot. IEEE Transactions on Automation Science and Engineering, 16(2), 506-517. Advance online publication. https://doi.org/10.1109/TASE.2018.2829927

Jensen, R. I. T. & Iosifidis, A. (2023). Qualifying and raising anti-money laundering alarms with deep learning. Expert Systems with Applications, 214, Article 119037. https://doi.org/10.1016/j.eswa.2022.119037

Muravev, A., Can Ozan, E., Iosifidis, A. & Gabbouj, M. (2017). Pyramid Encoding for Fast Additive Quantization. Proceedings of the European Signal Processing Conference, 2017, 2575-2579. https://doi.org/10.23919/EUSIPCO.2017.8081662

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

Iosifidis, A. & Gabbouj, M. (2016). Prototype-based Class-Specific Nonlinear Subspace Learning for Large-Scale Face Verification. In 2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016 Article 7820988 IEEE. https://doi.org/10.1109/IPTA.2016.7820988

Heidari, N. & Iosifidis, A. (2021). Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition. In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (Vol. 2021-June, pp. 3220-3224). IEEE. https://doi.org/10.1109/ICASSP39728.2021.9413860

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

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

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. 537-537)

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. (2017). 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

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. Advance online publication. 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

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)

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

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), 1-16. https://doi.org/10.1007/s11119-017-9497-6

Alvarez Tunon, O., Jardón, A. & Balaguer, C. (2017). Plataformas robóticas para la inspección de los pozos de las minas de carbón en las secciones inundadas y no inundadas. In Jornadas Nacionales de Robótica Spansk Robotics Conference

Nyholm Jørgensen, R. (Producer). (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.

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

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

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

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Revised 10.08.2023

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