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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Steen, K. A., Villa-Henriksen, A., Therkildsen, O. R., Karstoft, H. & Green, O. (2012). Automatic Detection of Animals using Thermal Imaging. Paper presented at Agriculture & Engineering for a Healthier Life, Valencia, Spain. http://cigr.ageng2012.org/images/fotosg/tabla_137_C0392.pdf

Steen, K. A., Grooters, K., Høilund, C., Rasmussen, J., Nyholm Jørgensen, R., Dyrmann, M. & Green, O. (2018). Automated Weed Intensity Mapping. In G. K. P.W.G. , C. Lokhorst, A. H. Ipema, C. Kempenaar, C. M. Groenestein , C. G. van Oostrum & N. J. Ros (Eds.), Book of Abstracts of the European Conference on Agricultural Engineering: AgEng2018 (pp. 109). Wageningen University. https://doi.org/10.18174/471678

Mehndiratta, M., Camci, E. & Kayacan, E. (2018). Automated Tuning of Nonlinear Model Predictive Controller by Reinforcement Learning. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018: IROS (pp. 3016-3021). Article 8594350 IEEE. https://doi.org/10.1109/IROS.2018.8594350

Asif, M. R., Maurya, P. K., Foged, N., Larsen, J. J., Auken, E. & Christiansen, A. V. (2022). Automated transient electromagnetic data processing for ground-based and airborne systems by a deep learning expert system. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 5919814. https://doi.org/10.1109/TGRS.2022.3202304

Asif, MR., Larsen, JJ. & Christiansen, AV. (2024). Automated Processing of Time-Domain Electromagnetic Data Influenced by Induced Polarization Effects. In NSG 2024 30th European Meeting of Environmental and Engineering Geophysics (pp. 1-5). European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.202420140

Asif, M. R., Kass, A., Westerhoff, R., Rawlinson, Z., Christiansen, A. V. & Bording, T. S. (2023). Automated Processing of a Large-Scale Airborne Electromagnetic Survey by Deep Learning. Paper presented at NSG2023 29th European Meeting of Environmental and Engineering Geophysics, Edinburgh , United Kingdom. https://doi.org/10.3997/2214-4609.202320057

Shah, A., Naqvi, S. S., Naveed, K., Salem, N., Khan, M. A. U. & Alimgeer, K. S. (2021). Automated Diagnosis of Leukemia: A Comprehensive Review. IEEE Access, 9, 132097-132124. https://doi.org/10.1109/ACCESS.2021.3114059

Christiansen, P., Steen, K. A., Nyholm Jørgensen, R. & Karstoft, H. (2014). Automated Detection and Recognition of Wildlife Using Thermal Cameras. Sensors, 14(8), 13778-13793. https://doi.org/10.3390/s140813778

Asif, M. R., Kass, A., Westerhoff, R., Rawlinson, Z. & Christiansen, A. V. (2023). Automated data processing of a large-scale airborne time-domain electromagnetic survey by a deep learning expert system. Paper presented at 8th International Workshop on Airborne Electromagnetics, Queensland, Australia.

Liu, L., Chen, I.-M., Kayacan, E., Kong Tiong, L. & Maruvanchery, V. (2015). Automated Construction Quality Assessment: A review. In Mechatronics and its Applications (ISMA), 2015 10th International Symposium on (pp. 1-6). IEEE. https://doi.org/10.1109/ISMA.2015.7373459

Sørensen, L. T., Schultz, N., Christensen, D. S., Jiang, B., Dalsgaard, P., Jensen, J. J., Larsen, L. B. & Pedersen, C. F. (2004). A user centred methodology for establishing PAN/PN scenarios. Paper presented at My Personal Adaptive Global NET: Visions and beyond, Shanghai, China.

Steen, K. A., Therkildsen, O. R., Green, O. & Karstoft, H. (2013). Audio-Visual Recognition of Goose Flocking Behavior. International Journal of Pattern Recognition and Artificial Intelligence, 27(7). https://doi.org/10.1142/S0218001413500201

Steen, K. A., Therkildsen, O. R., Karstoft, H. & Green, O. (2014). Audio-Based Detection and Recognition of Conflict Species in Outdoor Environments Using Pattern Recognition Methods. Applied Engineering in Agriculture, 30(1), 89-96. https://doi.org/10.13031/aea.30.9817

Bozcan, I. & Kayacan, E. (2020). AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance. In 2020 IEEE International Conference on Robotics and Automation (ICRA) (pp. 8504-8510). IEEE. https://doi.org/10.1109/ICRA40945.2020.9196845

H. Abiyev, R., Kaynak, O. & Kayacan, E. (2013). A Type-2 Fuzzy Wavelet Neural Network for System Identification and Control. Journal of the Franklin Institute, 350(7), 1658-1685. https://doi.org/10.1016/j.jfranklin.2013.04.020

Kass, M. A., Auken, E., Larsen, J. J. & Christiansen, A. V. (2021). A towed magnetic gradiometer array for rapid, detailed imaging of utility, geological, and archaeological targets. Geoscientific Instrumentation, Methods and Data Systems, 10(2), 313-323. https://doi.org/10.5194/gi-10-313-2021

Dalgaard, E. B., Christiansen, P., Larsen, J. J. & Auken, E. (2014). A temporal and spatial analysis of anthropogenic noise sources affecting SNMR. Journal of Applied Geophysics, 110, 34-42. https://doi.org/10.1016/j.jappgeo.2014.08.009

Bjerge, K., Fynbo, H. O. U. & Johansen, J. G. (2019). A system on programmable chip design of a digitizer with improved trapezoidal filter validation. Microprocessors and Microsystems, 65, 7-13. https://doi.org/10.1016/j.micpro.2018.12.004

Griffiths, M. P., Grombacher, D., Kass, M. A., Vang, M., Liu, L. & Larsen, J. J. (2023). A surface NMR forward in a dot product. Geophysical Journal International, 234(3), 2284-2290. https://doi.org/10.1093/gji/ggad203

HAO, H., WANG, H., REHMAN, N. UR. & TIAN, H. (2016). A Study of the Characteristics of MEMD for Fractional Gaussian Noise. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E99.A(6), 1228-1232. https://doi.org/10.1587/transfun.e99.a.1228

Saleem, S., Naqvi, S. S., Manzoor, T., Saeed, A., Rehman, N. U. & Mirza, J. (2019). A Strategy for Classification of "Vaginal vs. Cesarean Section" Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings. Frontiers in Physiology, 10. https://doi.org/10.3389/fphys.2019.00246

Kass, M. A., Grombacher, D., Griffiths, M., Vang, M. Ø., Liu, L. & Larsen, J. J. (2022). A steady-state approach to surface nuclear magnetic resonance. In Proceedings of the Symposium on the Application of Geophyics to Engineering and Environmental Problems, SAGEEP (pp. 54). J and N Group, Ltd.. https://www.eegs.org/assets/docs/Symposium/2022SAGEEP/AbstractsbyAuthor/Kass%2C%20A%20STEADY-STATE%20APPROACH%20TO%20SURFACE%20NUCLEAR%20MAGNETIC%20RESONANCE%20-%20SAGEEP%202022%20-%20205367449.pdf

Naveed, K., Akhtar, M. T., Siddiqui, M. F. & Rehman, N. U. (2021). A statistical approach to signal denoising based on data-driven multiscale representation. Digital Signal Processing, 108, Article 102896. https://doi.org/10.1016/j.dsp.2020.102896

Ma, X., J. Hegarty, P., Abildgaard Pedersen, J., Johansen, L. G. & Larsen, J. J. (2017). Assessing the Influence of Loudspeaker Driver Nonlinear Distortion on Personal Sound Zones. In 142nd Audio Engineering Society International Convention 2017 (Vol. 2, pp. 1019-1026). Article 9807 Audio Engineering Society . http://www.aes.org/e-lib/browse.cfm?elib=18681

Jensen, T., Karstoft, H., Green, O. & Munkholm, L. J. (2017). Assessing the effect of the seedbed cultivator leveling tines on soil surface properties using laser range scanners. Soil & Tillage Research, 167(April), 54-60. https://doi.org/10.1016/j.still.2016.11.006

Mehndiratta, M., Kayacan, E. & Kayacan, E. (2018). A Simple Learning Strategy for Feedback Linearization Control of Aerial Package Delivery Robot. In 2018 IEEE Conference on Control Technology and Applications, CCTA 2018: CCTA (pp. 361-367). Article 8511485 IEEE. https://doi.org/10.1109/CCTA.2018.8511485

Oniz, Y., Kayacan, E., Cisel Aras, A., Kaynak, O. & H. Abiyev, R. (2010). A servo system control with time-varying load using type-2 fuzzy neural system, In IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society (pp. 35-40). IEEE. https://doi.org/10.1109/IECON.2010.5675372

Kayacan, E., Oniz, Y., Cisel Aras, A., Kaynak, O. & H. Abiyev, R. (2011). A Servo System Control with Time-varying and Nonlinear Load Conditions Using Type-2 TSK Fuzzy Neural System. Applied Soft Computing, 11(8), 5735-5744. https://doi.org/10.1016/j.asoc.2011.03.008

Malekian, R., Thakur, A., Nair, L. & Pedersen, C. F. (2018). A Sensor Based Peer to Peer Vehicle Data Sharing System, An Internet of Vehicles Approach. Journal of Internet Technology, 19(7), 2155-2162. http://jit.niu.edu.tw/article/view/1865

Madsen, J. K., Karstoft, H., Hansen, F. O. & Toftegaard, T. S. (2010). ASE-BAN, a Wireless Body Area Network Testbed. In EMERGING 2010 : The Second International Conference on Emerging Network Intelligence. Proceedings (pp. 120-124). Xpert Publishing Service. http://www.thinkmind.org/index.php?view=article&articleid=emerging_2010_5_40_40059

Madsen, J. K., Karstoft, H., Toftegaard, T. S. & Hansen, F. O. (2010). ASE-BAN, a Wireless Body Area Network Testbed.

Bjerge, K., Schougaard, J. H. & Larsen, D. E. (2021). A scalable and efficient convolutional neural network accelerator using HLS for a system-on-chip design. Microprocessors and Microsystems, 87, Article 104363. https://doi.org/10.1016/j.micpro.2021.104363

Kayacan, E., Kayacan, E., Ramon, H. & Saeys, W. (2012). A Robust On-line Learning Algorithm for Type-2 Fuzzy Neural Networks and its Experimental Evaluation on an Autonomous Tractor. In Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on (pp. 1652-1657). IEEE. https://doi.org/10.1109/ICSMC.2012.6377974

Rasmussen, S., Nyboe, S. N., Mai, S. S. & Larsen, J. J. (2017). A Robust Framework for Cancellation of MSK Interference in TEM Data. In 2nd European Airborne Electromagnetics Conference 2017, Held at Near Surface Geoscience Conference and Exhibition 2017 (pp. 121-125). European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.201702172

Griffiths, M., Grombacher, D., Kass, M. A., Liu, L., Vang, M. & Larsen, J. J. (2022). A Reformulated Surface Nmr Forward For Multi-Sequence Acquisitions. In 28th European Meeting of Environmental and Engineering Geophysics, Held at the Near Surface Geoscience Conference and Exhibition 2022, NSG 2022 European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.202220109

Tøttrup, D., Skovgaard, S. L., Sejersen, J. L. F. & de Figueiredo, R. P. (2022). A Real-Time Method for Time-to-Collision Estimation from Aerial Images. Journal of Imaging, 8(3), Article 62. https://doi.org/10.3390/jimaging8030062

Giselsson, T. M., Nyholm Jørgensen, R., Jensen, P. K., Dyrmann, M. & Midtiby, H. S. (2017). A Public Image Database for Benchmark of Plant Seedling Classification Algorithms. arxiv.org. https://arxiv.org/pdf/1711.05458

Larsen, J. J., Liu, L., Grombacher, D., Osterman, G. & Auken, E. (2019). Apsu - A new surface nmr instrument for hydrogeophysics. Paper presented at 25th European Meeting of Environmental and Engineering Geophysics, Held at Near Surface Geoscience Conference and Exhibition 2019, NSG 2019, The Hague, Netherlands.

Larsen, J. J., Liu, L., Grombacher, D. J., Osterman, G. K. & Auken, E. (2018). Apsu - a new surface NMR instrument.. C01. Paper presented at 7th International Workshop on Magnetic Resonance, Jilin, China.

Larsen, J. J., Liu, L., Grombacher, D., Osterman, G. K. & Auken, E. (2020). Apsu — A new compact surface nuclear magnetic resonance system for groundwater investigation. Geophysics, 85(2), JM1-JM11. https://doi.org/10.1190/geo2018-0779.1

Larsen, J. J., Griffiths, M., Vang, M., Liu, L. & Grombacher, D. (2021). Apsu - A compact surface NMR instrument for groundwater investigations. SEG Technical Program Expanded Abstracts, 2021-September, 3135-3139. https://doi.org/10.1190/segam2021-3582046.1

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

Barzegar Gerdroodbary, M. & Salavatidezfouli, S. (2025). A predictive surrogate model of blood haemodynamics for patient-specific carotid artery stenosis. Journal of the Royal Society Interface, 22(224), Article 20240774. https://doi.org/10.1098/rsif.2024.0774

Salavatidezfouli, S., Rakhsha, S., Sheidani, A., Stabile, G. & Rozza, G. (2025). A predictive surrogate model for heat transfer of an impinging jet on a concave surface. International Journal of Heat and Mass Transfer, 251, Article 127248. https://doi.org/10.1016/j.ijheatmasstransfer.2025.127248

Barzegar Gerdroodbary, M. & Salavatidezfouli, S. (2025). A predictive surrogate model based on linear and nonlinear solution manifold reduction in cardiovascular FSI: A comparative study. Computers in Biology and Medicine, 189, Article 109959. https://doi.org/10.1016/j.compbiomed.2025.109959

Rehman, N. U., Xia, Y. & Mandic, D. P. (2010). Application of multivariate empirical mode decomposition for seizure detection in EEG signals. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology IEEE. https://doi.org/10.1109/iembs.2010.5626665

Thanh Nguyen, T., Keresztes, J., Vandevoorde, K., Kayacan, E., G. de Baerdemaeker, J. & Saeys, W. (2014). Apple detection algorithm for robotic harvesting using a RGB-D camera. In Proceedings International Conference of Agricultural Engineering (pp. 1-8) https://pdfs.semanticscholar.org/d294/58c25c8bde8492c15eacb96c871c2030f2c5.pdf

Andersen, P. A., Larsen, J. J. & Paulsen, H. N. (2002). A photonic crystal fiber with zero dispersion at 1064 nm. In ECOC 2002 (Vol. 2, pp. 1-2). I E E E . http://orbit.dtu.dk/fedora/objects/orbit:57209/datastreams/file_4272887/content

Cowan, B., Fu, C., Imanberdiyev, N., Dong, Y. & Kayacan, E. (2018). A Performance Evaluation of Methods for Visual Tracking. In The 14th International Conference on Control, Automation, Robotics and Vision (pp. 1-6). IEEE.

Ahmadieh Khanesar, M., Teshnelab, M., Kayacan, E. & Kaynak, O. (2010). A Novel Type-2 Fuzzy Membership Function: Application to the Prediction of Noisy. In Computational Intelligence for Measurement Systems and Applications (CIMSA), 2010 IEEE International Conference on (pp. 128-133). IEEE. https://doi.org/10.1109/CIMSA.2010.5611774

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