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

Sort by: Date | Author | Title

S. Nadimi, E., Blanes-Vidal, V., Jørgensen, R. N. & Christensen, S. (2011). Energy generation for an ad hoc wireless sensor network-based monitoring system using animal head movement. Computers and Electronics in Agriculture, 75(2), 238-242. https://doi.org/10.1016/j.compag.2010.11.008

Mandic, D. P., Rehman, N. U., Wu, Z. & Huang, N. E. (2013). Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis. IEEE Signal Processing Magazine, 30(6), 74-86. https://doi.org/10.1109/msp.2013.2267931

Ravelo, B., Cabral, J., Wagner, S. R., Pedersen, C. F. & Mathiesen, M. (2014). EMI and BER/PER Analysis of Wi-Fi and Bluetooth Communication for CRIP Platform. Paper presented at 17e Colloque International et Exposition sur la Compatibilité Electromagnetique, Clermont-Ferrand, France.

Ravelo, B., Cabral, J., Wagner, S. R., Pedersen, C. F. & Mathiesen, M. (2014). EMI and BER/PER Analysis of Wi-Fi and Bluetooth Communication for CRIP Platform. Poster session presented at 17e Colloque International et Exposition sur la Compatibilité Electromagnetique, Clermont-Ferrand, France.

Hajj, R., Ravelo, B., Cabral, J., Wagner, S. R., Pedersen, C. F. & Mathiesen, M. (2014). EMI Analysis of Wi-Fi and Bluetooth RF Communication for Medical Applications. In CEM 2014 - Actes du 17ème Colloque International et Exposition sur la Compatibilité ÉlectroMagnétique

Rehman, N. U., Park, C., Huang, N. E. & Mandic, D. P. (2013). Emd via memd: Multivariate noise-aided computation of standard emd. Advances in Adaptive Data Analysis, 05(02), 1350007. https://doi.org/10.1142/s1793536913500076

Kragh, M. F. & Karstoft, H. (2021). Embryo selection with artificial intelligence: how to evaluate and compare methods? Journal of Assisted Reproduction and Genetics, 38(7), 1675-1689. https://doi.org/10.1007/s10815-021-02254-6

Sørensen, R. A., Skovsen, S., Christiansen, P. & Karstoft, H. (2017). Embedded Visual Perception for Autonomous Agricultural Machines Using Lightweight Convolutional Neural Networks. International Journal of Agricultural and Biosystems Engineering, 11(4), 1296. Article 63060. https://www.waset.org/abstracts/63060

Kayacan, E., Coupland, S., John, R. & Ahmadieh Khanesar, M. (2017). Elliptic Membership Functions and the Modeling Uncertainty in Type-2 Fuzzy Logic Systems As Applied to Time Series Prediction. In 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 (pp. 1-7). Article 8015457 IEEE . https://doi.org/10.1109/FUZZ-IEEE.2017.8015457

Schmitt, M., Lochbrunner, S., Shaffer, J. P., Larsen, J. J., Zgierski, M. Z. & Stolow, A. (2001). Electronic continua in time-resolved photoelectron spectroscopy. II. Corresponding ionization correlations. Journal of Chemical Physics, 114(3), 1206-1213. https://doi.org/10.1063/1.1331637

Larsen, J. J., Johansen, L. G. & Karstoft, H. (2012). Electrocardiogram Derived Respiration using an Evolutionary Algorithm. In S. V. Huffel, C. M. B. A. Correia, A. L. N. Fred & H. Gamboa (Eds.), BIOSIGNALS 2012: Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (pp. 147-152). SCITEPRESS Digital Library.

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. Article 9246269. https://doi.org/10.1109/TGRS.2020.3029658

Larsen, J. J., Langhof, R. B., Kjær, M. W., Vang, M., Liu, L., Griffiths, M. & Grombacher, D. (2022). Efficient processing of surface NMR data with spectral analysis. Geophysical Journal International, 229(1), 286-298. https://doi.org/10.1093/gji/ggab472

Griffiths, M. P., Grombacher, D. J. & Larsen, J. J. (2021). Efficient numerical Bloch solutions for multipulse surface NMR. Geophysical Journal International, 227(3), 1905-1916. https://doi.org/10.1093/gji/ggab321

Vang, M. Ø., Grombacher, D., Larsen, J. J. & Wilson, S. (2025). Efficient mapping of complex groundwater systems associated with braided rivers using small coil surface nuclear magnetic resonance. Geophysics, 90(3), 1-12. https://doi.org/10.1190/geo2023-0765.1

Pham, X. H. (2023). Efficient deep learning-based monocular 3D perception for agile robot navigation. Århus Universitet.

Griffiths, M. P., Grombacher, D. & Larsen, J. J. (2020). Efficient Bloch-equation solvers for surface nuclear magnetic Resonance. Abstract from AGU Fall Meeting 2020.

Jørgensen, R. N. (2010). Effects of the machine wheel load on grass yield. In P. Savoie, J. Villeneuve & R. Morisette (Eds.), XVIIth World Congress of the International Commission of Agricultural and Biosystems Engineering: Book of Abstracts (Vol. XVII, pp. 221). CIGR. https://www.researchgate.net/publication/280227190_Effects_of_the_machine_wheel_load_on_grass_yield

Green, O., Jørgensen, R. N., Kristensen, K., Bochtis, D. & Sørensen, C. A. G. (2010). Effect of machinery wheel load on grass yield. World Congress of the International Commission of Agricultural and Biosystems Engineering (CIGR), XVII. http://www.bioeng.ca/cigr2010/

Sridharan, K. S., Johnsen, E. L., Nielsen, A. H., Beniczky, S., Johansen, L. G., Sunde, N. & Østergaard, K. (2015). Effect of deep brain stimulation (DBS) and medication on somatosensory function in Parkinson’s disease (PD). Poster session presented at Nordic Congress of Clinical Neurophysiology, Helsingør, Denmark.

Asif, M. R., Bording, T. S., Barfod, A. S., Grombacher, D. J., Maurya, P. K., Christiansen, A. V., Auken, E. & Larsen, J. J. (2021). Effect of Data Pre-Processing on the Performance of Neural Networks for 1-D Transient Electromagnetic Forward Modeling. IEEE Access, 9, 34635-34646. https://doi.org/10.1109/ACCESS.2021.3061761

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 from Near Surface Geoscience 2020.

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. In 26th European Meeting of Environmental and Engineering Geophysics, Held at Near Surface Geoscience 2020 Article 061 European Association of Geoscientists and Engineers, EAGE. https://doi.org/10.3997/2214-4609.202020061

Rehman, N., Ehsan, S., Naveed, K., McDonald-Maier, K. D. & Safdar, M. W. (2015). Dynamically sampled multivariate empirical mode decomposition. Electronics Letters, 51(24), 2049-2051. https://doi.org/10.1049/el.2015.1176

Rehman, N. U. & Mandic, D. P. (2014). Dynamically-Sampled Bivariate Empirical Mode Decomposition. IEEE Signal Processing Letters, 21(7), 857-861. https://doi.org/10.1109/lsp.2014.2317773

Naveed, K., Shaukat, B. & Rehman, N. U. (2018). Dual tree complex wavelet transform-based signal denoising method exploiting neighbourhood dependencies and goodness-of-fit test. Royal Society Open Science, 5(9), 180436. https://doi.org/10.1098/rsos.180436

Olesen, C. R., Nyholm Jørgensen, R., Laursen, M. S., Larsen, R. & Rosendahl Bjorholm, S. (2017, Jun 1). Droner og losurin. Danmarks Jægerforbund. http://www.jaegerforbundet.dk/om-dj/nyhedsarkiv/2017/droner-og-losurin/

Mortensen, A. K., Laursen, M. S., Nyholm Jørgensen, R. & Gislum, R. (2019). Drone dataflow - a MATLAB toolbox for extracting plots from images captured by a UAV. In J. V. Stafford (Ed.), Precision Agriculture '19 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019 (pp. 959-965). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-888-9_118

Schultz, N. (Ed.), Tan, S. (Ed.), Sørensen, J. K. (Ed.), Karamolegkos, P., Larsen, J. E., Larsen, L. B., Nikolakopoulos, G., Olseni, C., Patriakis, C. Z., Pedersen, C. F., Pedersen, J. S., Proschowsky, M., Protonotarios, V., Roswall, R., Saugstrup, D., Sørensen, L., Bessler, S., Hammershøj, A., Heinze, E. ... Xu, C. (2006). Draft User Functionalities and Interfaces of PN Services (Low-Fi Prototyping). Information Society Technologies, IST-FP6-IP-027396, My Personal Adaptive Global NET (MAGNET) Beyond, EU.

Olsson, P.-I., Fiandaca, G., Larsen, J. J., Dahlin, T. & Auken, E. (2016). Doubling the spectrum of time-domain induced polarization: removal of non-linear self-potential drift, harmonic noise and spikes, tapered gating, and uncertainty estimation. Abstract from IP2016 - 4th International Workshop on Induced Polarization, Aarhus , Denmark.

Fiandaca, G., Olsson, P.-I., Auken, E., Larsen, J. J., Maurya, P. K. & Dahlin, T. (2015). Doubling the Spectrum of Time-Domain Induced Polarization: Removal of Harmonic Noise and Self-Potential Drift. Abstract from AGU Fall Meeting 2015, San Fransisco, United States. https://agu.confex.com/agu/fm15/meetingapp.cgi/Paper/75469

Fiandaca, G., Olsson, P. I., Larsen, J. J., Dahlin, T. & Auken, E. (2016). Doubling the spectrum of time-domain induced polarization by harmonic de-noising, drift/spike removal and tapered gating. In 22nd European Meeting of Environmental and Engineering Geophysics, Near Surface Geoscience 2016 European Association of Geoscientists and Engineers, EAGE. https://doi.org/10.3997/2214-4609.201602021

Olsson, P.-I., Fiandaca, G., Larsen, J. J., Dahlin, T. & Auken, E. (2016). Doubling the spectrum of time-domain induced polarization by harmonic de-noising, drift correction, spike removal, tapered gating and data uncertainty estimation. Geophysical Journal International, 207(2), 774-784. Article ggw260. https://doi.org/10.1093/gji/ggw260

Sarabakha, A., Fu, C. & Kayacan, E. (2017). Double-Input Interval Type-2 Fuzzy Logic Controllers: Analysis and Design. In 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 (pp. 1-6). Article 8015485 IEEE . https://doi.org/10.1109/FUZZ-IEEE.2017.8015485

Asif, M. R., Foged, N., Bording, T. S., Larsen, J. J. & Christiansen, A. V. (2023). DL-RMD: a geophysically constrained electromagnetic resistivity model database for deep learning applications. Earth System Science Data, 15(3), 1389-1401. https://doi.org/10.5194/essd-2022-345, https://doi.org/10.5194/essd-15-1389-2023

Kayacan, E., Kayacan, E., Ramon, H. & Saeys, W. (2014). Distributed nonlinear model predictive control of an autonomous tractor-trailer. Mechatronics, 24(8), 926-933. https://doi.org/10.1016/j.mechatronics.2014.03.007

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), Article 2671. https://doi.org/10.3390/rs11222671

So, P., Sarabakha, A., Wu, F., Culha, U., Abu-Dakka, F. & Haddadin, S. (2024). Digital Robot Judge: Building a Task-centric Performance Database of Real-World Manipulation With Electronic Task Boards. Robotics Automation Magazine, IEEE.

Sridharan, K. S., Højlund, A., Johnsen, E. L., Sunde, N., Johansen, L. G., Beniczky, S. & Østergaard, K. (2017). Differentiated effects of deep brain stimulation and medication on somatosensory processing in Parkinson's disease. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 128(7), 1327-1336. https://doi.org/10.1016/j.clinph.2017.04.014

Laursen, M. S., Nyholm Jørgensen, R., Midtiby, H. S., Jensen, K., Christiansen, M. P., Giselsson, T. M., Mortensen, A. K. & Jensen, P. K. (2016). Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops. Sensors, 16(11), Article 1848. https://doi.org/10.3390/s16111848

Laursen, M. S., Nyholm Jørgensen, R., Skov Midtiby, H., Mortensen, A. K. & Santhome, S. (2017). Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops: Statistical Evaluation of the Potential Herbicide Savings. International Journal of Agricultural and Biosystems Engineering, 11(4), 1285. https://www.waset.org/abstracts/62424

Jensen, K. L., Pedersen, C. F. & Larsen, L. B. (2007). Diasnet Mobile - A Personalized Mobile Diabetes Management and Advisory Service. Paper presented at Conference on User Modelling, Corfu, Greece. http://www.iit.demokritos.gr/um2007/UM2007_WS7_PEL.pdf

Ahrendt, P., Gregersen, T. & Karstoft, H. (2011). Development of a real-time computer vision system for tracking loose-housed pigs. Computers and Electronics in Agriculture, 76(2), 169-174. https://doi.org/10.1016/j.compag.2011.01.011

Yan, R.-J., Leong Low, C., Duan, J., Liu, L., Kayacan, E., Chen, I.-M. & Tiong, R. (2017). Development of a Novel Post-Construction Quality Assessment Robot System. In 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016 (pp. 1-6). Article 7838741 IEEE . https://doi.org/10.1109/ICARCV.2016.7838741

Jørgensen, R. (2006). Det lugter af rævekage. Horsens Folkeblad.

Dyrmann, M., Nyholm Jørgensen, R. & Midtiby, H. S. (2017). Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network. Advances in Animal Biosciences, 8(2), 842-847. https://doi.org/10.1017/S2040470017000206

Thanh Nguyen, T., Vandevoorde, K., Wouters, N., Kayacan, E., G. de Baerdemaeker, J. & Saeys, W. (2016). Detection of red and bicoloured apples on tree with an RGB-D camera. Biosystems Engineering, 146, 33-44. https://doi.org/10.1016/j.biosystemseng.2016.01.007

Frederiksen, T. & Larsen, J. J. (2019). Detection of Capacitive Couplings in Ground-Based TEM Data with a 1D Convolutional Neural Network. 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.

Steen, K. A., Therkildsen, O. R., Green , O. & Karstoft, H. (2015). Detection of Bird Nests during Mechanical Weeding by Incremental Background Modeling and Visual Saliency. Sensors, 15(3), 5096-5111. https://doi.org/10.3390/s150305096

Chen, Q., Lang, X., Lu, S., Rehman, N. U., Xie, L. & Su, H. (2021). Detection and root cause analysis of multiple plant-wide oscillations using multivariate nonlinear chirp mode decomposition and multivariate Granger causality. Computers and Chemical Engineering, 147, Article 107231. https://doi.org/10.1016/j.compchemeng.2021.107231

Displaying results 551 to 600 out of 856

Back to the main page

Revised 10.08.2023

AU Engineering