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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Pedersen, C. F., Brix, L. C., Hansen, K. V., Haase, J. & Larsen, O. V. (2002). Modeling interaction between a brain spatula and a human brain. Child's Nervous System, 18(5), 257-260. https://doi.org/10.1007/s00381-002-0581-4

Kayacan, E., Kayacan, E., Ramon, H. & Saeys, W. (2013). Modeling and Identification of the Yaw Dynamics of an Autonomous Tractor. In Control Conference (ASCC), 2013 9th Asian (pp. 1-6). IEEE. https://doi.org/10.1109/ASCC.2013.6606388

Larsen, J. J. (2016). Model-based subtraction of spikes from surface nuclear magnetic resonance data. Geophysics, 81(4), WB1-WB8. https://doi.org/10.1190/geo2015-0442.1

Dalgaard, E., Auken, E. & Larsen, J. J. (2012). Model-based noise cancelling of multichannel magnetic resonance sounding measurements. In Near Surface Geoscience 2012 European Association of Geoscientists and Engineers, EAGE. http://www.scopus.com/inward/record.url?scp=84898482260&partnerID=8YFLogxK

Feng, H., Gomes, C., Iosifidis, A. & Larsen, P. G. (2021). Model-based monitoring and state estimation for digital twins: The Kalman filter. In C. R. Martin, M. J. Blas & A. I. Psijas (Eds.), Annual Modeling and Simulation Conference (ANNSIM 2021) (pp. 1-2)

Larsen, J. J., Dalgaard, E. B. & Auken, E. (2012). Model-based cancelling of powerline harmonics in multichannel MRS recordings. Abstract from Magnetic Resonance in the Subsurface, Hannover, Germany.

Skovsen, S. K. (2021). Mixed Crop Mapping by Species Discrimination using Data Driven Computer Vision and Simulated Data. Aarhus Universitet.

Ma, X., J. Hegarty, P. & Larsen, J. J. (2018). Mitigation of Nonlinear Distortion in Sound Zone Control by Constraining Individual Loudspeaker Driver Amplitudes. I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings, 2018, 456-460. https://doi.org/10.1109/ICASSP.2018.8462135

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. Advance online publication. https://doi.org/10.1109/LGRS.2020.3000639

Sørensen, C. A. G., Bak, T. & Jørgensen, R. (2004). Mission planner for agricultural robotics. In AgEng 2004, Leuven, Belgium. Book of Abstracts, ISBN 90-76019-258, 894-895. Paper on CD, 8 pp. (pp. 8-8)

Gautam, C., Tiwari, A. & Iosifidis, A. (2019). Minimum Variance-Embedded Multi-layer Kernel Ridge Regression for One-class Classification. In S. Sundaram (Ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 (pp. 389-396). Article 8628692 IEEE. https://doi.org/10.1109/SSCI.2018.8628692

Iosifidis, A., Tefas, A. & Pitas, I. (2013). Minimum Class Variance Extreme Learning Machine for Human Action Recognition. IEEE Transactions on Circuits and Systems for Video Technology, 23(11), 1968-1979. https://doi.org/10.1109/TCSVT.2013.2269774

Ntakaris, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2020). Mid-price prediction based on machine learning methods with technical and quantitative indicators. PLOS ONE, 15(6), Article e0234107. https://doi.org/10.1371/journal.pone.0234107

Lochbrunner, S., Larsen, J. J., Shaffer, J. P., Schmitt, M., Schultz, T., Underwood, J. G. & Stolow, A. (2000). Methods and applications of femtosecond time-resolved photoelectron spectroscopy. Journal of Electron Spectroscopy and Related Phenomena, 112(1-3), 183-198. https://doi.org/10.1016/S0368-2048(00)00212-7

Jørgensen, R., Sørensen, C. A. G., Søgaard, H. T., Kristensen, K., Green, O. & Christensen, S. (2007). Methodology for a Labor Extensive and Semi-Automated Field Trial Design Using Autoguidance and Comventional Machinery. In J. V. Stafford (Ed.), Ikke angivet (pp. 441-449). Wageningen Academic Publishers.

Nyholm Jørgensen, R., Steen, K. A. & Olesen, C. R. (2015). Method for removing animals from a field. (Patent No. DK2015/050155).

Christiansen, M. P. & Nyholm Jørgensen, R. (2015). Method for recording and predicting position data for a selfpropelled wheeled vehicle, and delivery or pick up system comprising a self-propelled, self-guided wheeled vehicle. (Patent No. DK178276 (B1) ).

Jørgensen, R. N., Green, O., Gislum, R. & Sørensen, C. A. G. (2010). Method for optimizing harvesting of crops. (Patent No. WO/2010/003421). http://www.wipo.int/pctdb/en/wo.jsp?WO=2010003421&IA=DK2009050150&DISPLAY=DESC

Jørgensen, R. N., Green, O., Sørensen, C. A. G. & Gislum, R. (2008). Method for optimizing harvesting of crops. (Patent No. PA 2008 00955).

S. Nadimi, E., Jørgensen, R. N. & Oudshoorn, F. W. (2011). Method and system for estimating herbage uptake of an animal. (Patent No. PCT/DK2010/050334).

Nyholm Jørgensen, R. (Producer). (2017). Metatilgangen til Deep Learning. Pictures, Video and sound recordings (digital), YouTube. https://www.youtube.com/watch?v=moLU3QvWIRM&t=355s

Iosifidis, A., Tefas, A. & Pitas, I. (2015). Merging Linear Discriminant Analysis with Bag of Words model for Human Action Recognition. In Image Processing (ICIP), 2015 IEEE International Conference on (pp. 832-836). IEEE. https://doi.org/10.1109/ICIP.2015.7350916

Lang, X., Rehman, N. U., Zhang, Y., Xie, L. & Su, H. (2020). Median ensemble empirical mode decomposition. Signal Processing, 176, Article 107686. https://doi.org/10.1016/j.sigpro.2020.107686

Kicanaoglu, B., Iosifidis, A. & Gabbouj, M. (2016). Maximum Margin Binary Classifiers using Intrinsic and Penalty graphs. In 2016 24th European Signal Processing Conference, EUSIPCO 2016 (pp. 2270-2274). Article 7760653 IEEE. https://doi.org/10.1109/EUSIPCO.2016.7760653

Tsapanos, N., Iosifidis, A. & Pitas, I. (2015). Mathematical preliminaries: graphs and matrices. In I. Pitas (Ed.), Graph-Based Social Media Analysis CRC Press.

Bajovic, D., Bakhtiarnia, A., Bravos, G., Brutti, A., Burkhardt, F., Cauchi, D., Chazapis, A., Cianco, C., Dall'Asen, N., Delic, V., Dimou, C., Djokic, D., Escobar-Molero, A., Esterle, L., Eyben, F., Farella, E., Festi, T., Geromitsos, A., Giakoumakis, G. ... Zammit, J. (2021). MARVEL: Multimodal Extreme Scale Data Analytics for Smart Cities Environments. In 2021 International Balkan Conference on Communications and Networking, BalkanCom 2021 (pp. 143-147). IEEE. https://doi.org/10.1109/BalkanCom53780.2021.9593258

Somerville, G. J., Nyholm Jørgensen, R., Bojer, O. M., Rydahl, P., Dyrmann, M., Andersen, P., Jensen, N-P. & Green, O. (2019). Marrying futuristic weed mapping with current herbicide sprayer capacities. In J. V. Stafford (Ed.), Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019 (pp. 231-237). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-888-9_28

Jensen, B-E., Andersen, H. J. & Jørgensen, R. (2004). Markrobotten arbejder. Agrologisk, 10, 12-13.

Green, O. & Jørgensen, R. (2007). Markforsøg med hundredvis af parceller. Bovilogisk, (5), 25.

Larsen, J. J., Dalgaard, E. B., Christiansen, P. & Auken, E. (2015). Mapping of electromagnetic noise in a magnetic resonance sounding context. In ASEG Extended Abstracts - 24th International Geophysical Conference and Exhibition: Geophysics and Geology Together for Discovery (Vol. 2015/1). CSIRO Publishing. https://doi.org/10.1071/ASEG2015ab241

Skovsen, S., Laursen, M. S., Nyholm Jørgensen, R. & Karstoft, H. (2018). Mapping of Clover Grass Leys using Image Recognition. Poster session presented at DTU and DIKU Summer School: Optimization in Image Analysis, Denmark. http://optimization-image-analysis.compute.dtu.dk/posters/soren_skovsen.pdf

Chavez Plascencia, A. & Karstoft, H. (2014). Map Building based on a Xtion Pro Live RGBD and a Laser Sensors. Journal of Information Technology & Software Engineering, 4, Article 126. https://doi.org/10.4172/2165-7866.1000126

Nousi, P., Tsantekidis, A., Passalis, N., Ntakaris, A., Kanniainen, J., Tefas, A., Gabbouj, M. & Iosifidis, A. (2019). Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data. IEEE Access, 7, 64722 - 64736 . Article 8713851. https://doi.org/10.1109/ACCESS.2019.2916793

Bording, T. S., Asif, M. R., Barfod, A. S., Larsen, J. J., Zhang, B., Grombacher, D. J., Christiansen, A. V., Engebretsen, K. W., Pedersen, J. B., Maurya, P. K. & Auken, E. (2021). Machine learning based fast forward modelling of ground-based time-domain electromagnetic data. Journal of Applied Geophysics, 187, Article 104290. https://doi.org/10.1016/j.jappgeo.2021.104290

Chumachenko, K., Mannisto, A., Iosifidis, A. & Raitoharju, J. (2020). Machine Learning Based Analysis of Finnish World War II Photographers. IEEE Access, 8, 144184-144196. Article 9159591. https://doi.org/10.1109/ACCESS.2020.3014458

Islam, M. T., Khan, H. A., Naveed, K., Nauman, A., Gulfam, S. M. & Kim, S. W. (2023). LUVS-Net: A Lightweight U-Net Vessel Segmentor for Retinal Vasculature Detection in Fundus Images. Electronics, 12(8), Article 1786. https://doi.org/10.3390/electronics12081786

Shabani, M. & Iosifidis, A. (2020). Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting. In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 2156-2161). Article 9308440 IEEE. https://doi.org/10.1109/SSCI47803.2020.9308440

Liu, L., Grombacher, D. J., Auken, E. & Larsen, J. J. (2018). Low noise, multichannel surface NMR receiver system with wireless connections to receiver coils. Abstract from First Australian Exploration Geoscience Conference, Sydney, Australia. http://www.publish.csiro.au/ex/pdf/ASEG2018abT7_1H

Klausen, K. C., Pagels, K. Ø., Bjerre, J. (Ed.) & Pedersen, C. F. (Ed.) (2019). LoRa and LoRaWAN Case Studies. European Regional Development Fund and Central Denmark Region.

Tu, G. J. & Karstoft, H. (2015). Logarithmic dyadic wavelet transform with its applications in edge detection and reconstruction. Applied Soft Computing, 26, 193-201. https://doi.org/10.1016/j.asoc.2014.09.044

Christiansen, M. P., Jensen, K., Ellekilde, L-P. & Jørgensen, R. N. (2011). Localization in orchards using Extended Kalman Filter for sensor-fusion - A FroboMind component.. http://www.njf.nu/site/seminarRedirect.asp?intSeminarID=441&p=1004

Jørgensen, R. & Rasmussen, P. (2002). Line Imaging Spectrometer Sampler (LISS) - a user manual.

Asif, M. R., Qi, C., Wang, T., Sadiq Fareed, M. & Ali Reza, S. (2019). License Plate Detection for Multi-national Vehicles: An Illumination Invariant Approach in Multi-lane Environment. Computers & Electrical Engineering, 78, 132-147. https://doi.org/10.1016/j.compeleceng.2019.07.012

Asif, M. R., Qi, C., Wang, T., Fareed, M. S. & Khan, S. (2019). License plate detection for multi-national vehicles – a generalized approach. Multimedia Tools and Applications, 78(24), 35585-35606. https://doi.org/10.1007/s11042-019-08199-4

Ahmadieh Khanesar, M., Kayacan, E., Teshnelab, M. & Kaynak, O. (2011). Levenberg Marquardt Algorithm for the Training of Type-2 Fuzzy Neuro Systems with a Novel Type-2 Fuzzy Membership Function. In Advances in Type-2 Fuzzy Logic Systems (T2FUZZ), 2011 IEEE Symposium on (pp. 88-93). IEEE. https://doi.org/10.1109/T2FUZZ.2011.5949558

Ahmadieh Khanesar, M. & Kayacan, E. (2015). Levenberge-Marquardt Training Method for Type-2 Fuzzy Neural Networks and Its Stability Analysis. In Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on (pp. 1-7). IEEE. https://doi.org/10.1109/FUZZ-IEEE.2015.7338101

Kusk, K., Nielsen, D. B., Thylstrup, T., Rasmussen, N. H., Jørvang, J., Pedersen, C. F. & Wagner, S. R. (2012). Lessons Learned From a Lightweight Context-Aware System for Achieving Reliable Home Blood Pressure Self-Measurements. Paper presented at NordiCHI 2012, The Nordic Conference on Human-Computer Interaction, Copenhagen, Denmark.

Tzogka, C., Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2019). Less is more: Deep Learning using subjective annotations for sentiment analysis from social media. In 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019: Proceedings Article 8918792 IEEE. https://doi.org/10.1109/MLSP.2019.8918792

Pedersen, C. F. (2011). Leja ordering LSFs for accurate estimation of predictor coefficients. Proceedings of the International Conference on Spoken Language Processing.

Jørgensen, R. N. (2011). Leg med store perspektiver. Land&Liv, 2011(uge 25), 13. http://www.e-pages.dk/landogliv/5/13

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Revised 10.08.2023

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