Institut for Elektro- og Computerteknologi

Aarhus Universitets segl

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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Høye, T. T., Ärje, J., Bjerge, K., Hansen, O. L. P., Iosifidis, A., Leese, F., Mann, H. M. R., Meissner, K., Melvad, C. & Raitoharju, J. (2021). Deep learning and computer vision will transform entomology. Proceedings of the National Academy of Sciences (PNAS), 118(2), Artikel e2002545117. https://doi.org/10.1073/pnas.2002545117
Bjerge, K., Nielsen, J. B., Sepstrup, M. V., Helsing-Nielsen, F. & Høye, T. T. (2021). An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning. Sensors (Switzerland), 21(2), Artikel 343. https://doi.org/10.3390/s21020343
Skovsen, S. K., Laursen, M. S., Kristensen, R. K., Rasmussen, J., Dyrmann, M., Eriksen, J., Gislum, R., Nyholm Jørgensen, R. & Karstoft, H. (2021). Robust Species Distribution Mapping of Crop Mixtures Using Color Images and Convolutional Neural Networks. Sensors, 21( 1), Artikel 175. https://doi.org/10.3390/s21010175
Jakobsen, M. O., Herskind, E. S., Bjerge, K., Ahrendt, P., Pedersen, C. F. & Knudsen, M. B. (2021). Vibration signatures in ball bearings as a function of lubricant viscosity ratio κ, under alternating lubrication conditions. Tribology International, 156, Artikel 106840. https://doi.org/10.1016/j.triboint.2020.106840
Lindahl Petersen , K., Ladegaard Jensen, K., Back Nielsen, M., Pas, L.-C., Jensen, N.-P., Nielsen, P. R., Bøjer, O. M., Nyholm Jørgensen, R., Laursen, M. S., Teimouri, N. & Hartmann, B. (2021). Analyse af mulige herbicidbesparelser ved brug af erfaringer og data fra RoboWeedMaPS. Datalogisk Institut. https://datalogisk.dk/wp-content/uploads/2021/03/MST_20rapport_version_2_2002092021.pdf
Asif, M. R., Bording, T. S., Barfod, A. S., Auken, E. & Larsen, J. J. (2020). Effect of Data Normalization on Neural Networks for the Forward Modelling of Transient Electromagnetic Data,. Abstract fra Near Surface Geoscience 2020.
Griffiths, M. P., Grombacher, D. & Larsen, J. J. (2020). Efficient Bloch-equation solvers for surface nuclear magnetic Resonance. Abstract fra AGU Fall Meeting 2020.
Griffiths, M. P., Grombacher, D. & Larsen, J. J. (2020). Parallelized Hybrid Bloch Solver for Surface Nuclear Magnetic Resonance. Abstract fra Near Surface Geoscience 2020. https://doi.org/10.3997/2214-4609.202020063
Grombacher, D., Liu, L., Osterman, G. K. & Larsen, J. J. (2021). Mitigating Narrowband Noise Sources Close to the Larmor Frequency in Surface NMR. IEEE Geoscience and Remote Sensing Letters, 18(8), 1376-1380. https://doi.org/10.1109/LGRS.2020.3000639
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
Jeppesen, J. H., Jacobsen, R. H. & Jorgensen, R. N. (2020). Crop Type Classification based on Machine Learning with Multitemporal Sentinel-1 Data. I A. Trost, A. Zemva & A. Skavhaug (red.), 2020 23rd Euromicro Conference on Digital System Design (DSD) (s. 557-564). IEEE. https://doi.org/10.1109/DSD51259.2020.00092
Barfod, A. A. S., Lévy, L. & Larsen, J. J. (2021). Automatic processing of time domain induced polarization data using supervised artificial neural networks. Geophysical Journal International, 224(1), 312-325. https://doi.org/10.1093/gji/ggaa460
Nielsen, A. H., Wagner, A., Iosifidis, A. & Karstoft, H. (2020). Cloud Nowcasting on Satellite Images: A Novel Dataset and Experimental Comparisons. https://ams.confex.com/ams/2020Annual/meetingapp.cgi/Paper/365696
Nielsen, A. H., Iosifidis, A. & Karstoft, H. (2020). CloudCast: A Satellite-Based Dataset and Baseline for Forecasting Clouds. Abstract fra European Conference on Computer Vision Workshop . https://drive.google.com/file/d/1_TV20JCIyBaetN6Y1mt7K5FeMx431Rr3/view
Asif, M. R., Bording, T. S., Barfod, A. A. S., Zhang, B., Larsen, J. J. & Auken, E. (2020). Improving computational efficiency of forward modelling for ground-based time-domain electromagnetic data using neural networks. Abstract fra EGU:General assembly 2020 , Vienna , Østrig. https://doi.org/10.5194/egusphere-egu2020-7067
Lang, X., Rehman, N. U., Zhang, Y., Xie, L. & Su, H. (2020). Median ensemble empirical mode decomposition. Signal Processing, 176, Artikel 107686. https://doi.org/10.1016/j.sigpro.2020.107686
Rehman, N. U., Naveed, K. & Khan, B. (2019). Data-Driven Multivariate Signal Denoising Using Mahalanobis Distance. IEEE Signal Processing Letters, 26(9), 1408-1412. https://doi.org/10.1109/LSP.2019.2932715
Rehman, N. U. & Aftab, H. (2019). Multivariate Variational Mode Decomposition. IEEE Transactions on Signal Processing, 67(23), 6039-6052. Artikel 8890883. https://doi.org/10.1109/TSP.2019.2951223
Gul, S., Siddiqui, M. F. & Rehman, N. U. (2020). FPGA-Based Design for Online Computation of Multivariate Empirical Mode Decomposition. IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 67(12), 5040-5050. Artikel 9159690. https://doi.org/10.1109/TCSI.2020.3012351
Naveed, K. & Rehman, N. U. (2020). Wavelet based multivariate signal denoising using Mahalanobis distance and EDF statistics. IEEE Transactions on Signal Processing, 68, 5997-6010. Artikel 9234624. https://doi.org/10.1109/TSP.2020.3029659
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, Artikel 102896. https://doi.org/10.1016/j.dsp.2020.102896
Morales, T., Sarabakha, A. & Kayacan, E. (2020). Image Generation for Efficient Neural Network Training in Autonomous Drone Racing. I 2020 International Joint Conference on Neural Networks (IJCNN) Artikel 9206943 IEEE. https://doi.org/10.1109/IJCNN48605.2020.9206943
Kass, M. A., Christiansen, A. V., Auken, E. & Larsen, J. J. (2021). Efficient reduction of powerline signals in magnetic data acquired from a moving platform. IEEE Transactions on Geoscience and Remote Sensing, 59(8), 7137-7146. Artikel 9246269. https://doi.org/10.1109/TGRS.2020.3029658
Afshari-Igder, M., Rafiei Foroushani, M., Boudjadar, J. & Khooban, M. H. (2021). Reliability and Safety Improvement of Emission-Free Ships: Systemic Reliability Centered Maintenance. IEEE Transactions on Transportation Electrification, 7(1), 256-266. Artikel 9220168. https://doi.org/10.1109/TTE.2020.3030082
Skovsen, S. K., Haraldsson, H., Davis, A., Karstoft, H. & Belongie, S. (2020). Decoupled Localization and Sensing with HMD-based AR for Interactive Scene Acquisition. I 2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (s. 167-171). Artikel 9288396 IEEE. https://doi.org/10.1109/ISMAR-Adjunct51615.2020.00053
Farkhani, S., Skovsen, S. K., Mortensen, A. K., Laursen, M. S., Nyholm Jørgensen, R. & Karstoft, H. (2020). Initial evaluation of enriching satellite imagery using sparse proximal sensing in precision farming. I C. M. U. Neale & A. Maltese (red.), Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII (Bind 11528). Artikel 115280A SPIE - International Society for Optical Engineering. https://doi.org/10.1117/12.2573626
Liu, L., Grombacher, D., Osterman, G., Auken, E. & Larsen, J. J. (2019). Vs data space retrieval and inversion for surface NMR. Afhandling præsenteret på 25th European Meeting of Environmental and Engineering Geophysics, Held at Near Surface Geoscience Conference and Exhibition 2019, NSG 2019, The Hague, Holland.
Larsen, J. J., Liu, L., Grombacher, D., Osterman, G. & Auken, E. (2019). Apsu - A new surface nmr instrument for hydrogeophysics. Afhandling præsenteret på 25th European Meeting of Environmental and Engineering Geophysics, Held at Near Surface Geoscience Conference and Exhibition 2019, NSG 2019, The Hague, Holland.
Banaei, M., Ghanami, F., Rafiei Foroushani, M., Boudjadar, J. & Khooban, M. H. (2020). Energy Management of Hybrid Diesel/Battery Ships in Multidisciplinary Emission Policy Areas. Energies, 13(16), Artikel 4179. https://doi.org/10.3390/en13164179
Hansen, B., Christiansen, A. V., Dalgaard, T., Jørgensen, F., Iversen, B. V., Larsen, J. J., Kjærgaard, C., Jacobsen, B. H., Auken, E., Hojberg, A. L. & Schaper, S. (2020). Danish review on advances in assessing: N retention in the subsurface in relation to future targeted N-regulation of agriculture. GEUS, Geological Survey of Denmark and Greenland. GEUS Rapport Bind 2020 Nr. 11 https://mapfield.dk/media/21858/d26_synthesis_report_mapfield.pdf
Sarabakha, A. & Kayacan, E. (2020). Online Deep Fuzzy Learning for Control of Nonlinear Systems Using Expert Knowledge. IEEE Transactions on Fuzzy Systems, 28(7), 1492-1503. https://doi.org/10.1109/TFUZZ.2019.2936787
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. Artikel 8298533. 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. Artikel 8764402. 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. Artikel 9046850. https://doi.org/10.1109/TTE.2020.2983247
Farkhani, S., Kragh, M. F., Christiansen, P. H., Nyholm Jørgensen, R. & Karstoft, H. (2019). Sparse-to-Dense Depth Completion in Precision Farming. I Proceedings of the 3rd International Conference on Vision, Image and Signal Processing, ICVISP 2019: Proceedings of the 3rd International Conference on Vision, Image and Signal Processing Artikel 35 Association for Computing Machinery. https://doi.org/10.1145/3387168.3387230
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, Artikel 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., 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), Artikel 1246. https://doi.org/10.3390/rs12081246
Raza, M. A., Qi, C., Asif, M. R. & Khan, M. A. (2020). An adaptive approach for multi-national vehicle license plate recognition using multi-level deep features and foreground polarity detection model. Applied Sciences, 10(6), Artikel 2165. https://doi.org/10.3390/app10062165
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
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.
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

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Revideret 13.11.2025
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AU Engineering