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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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Kjaer-Rasmussen, A., Griffiths, M. P., Grombacher, D. & Larsen, J. J. (2022). Fast removal of powerline harmonic noise from surface NMR datasets using a projection-based approach on graphical processing units. IEEE Geoscience and Remote Sensing Letters, 19, Article 8024005. https://doi.org/10.1109/LGRS.2021.3118064

Larsen, J. J., Dalgaard, E., Christiansen, P. & Auken, E. (2014). Fast mapping and analysis of anthropogenic noise sources affecting MRS experiments. In Near Surface Geoscience 2014 - 20th European Meeting of Environmental and Engineering Geophysics European Association of Geoscientists and Engineers. http://www.scopus.com/inward/record.url?scp=84908375168&partnerID=8YFLogxK

Tsapanos, N., Tefas, A., Nikolaidis, N., Iosifidis, A. & Pitas, I. (2015). Fast Kernel Matrix Computation for Big Data Clustering. In Procedia Computer Science (pp. 2445-2452). ELSEVIER. https://doi.org/10.1016/j.procs.2015.05.352

Dong, Y., Camci, E. & Kayacan, E. (2018). Faster RRT-based Nonholonomic Path Planning in 2D Building Environments Using Skeleton-constrained Path Biasing. Journal of Intelligent and Robotic Systems, 89(3-4), 387-401. https://doi.org/10.1007/s10846-017-0567-9

Miranda, J., Memon, M., Cabral, J., Ravelo, B., Wagner, S. R., Pedersen, C. F., Mathiesen, M. & Nielsen, C. (2017). Eye on Patient Care: Continuous Health Monitoring: Design and Implementation of a Wireless Platform for Healthcare Applications. IEEE Microwave Magazine, 18(2), 83-94. Article 7843732. https://doi.org/10.1109/MMM.2016.2635898

Iosifidis, A. (2015). Extreme Learning Machine based Supervised Subspace Learning. Neurocomputing, 167, 158-164. https://doi.org/10.1016/j.neucom.2015.04.083

Vang, M. Ø., Grombacher, D., Larsen, J. J. & Christiansen, A. V. (2024). Extrapolating sparse SNMR data sets onto dense TEM coverage using spatially variable correlations. Abstract from AGU Fall meeting 2024, Washington DC, United States.

Rasmussen, S., Skovgaard Nyboe, N., Mai, S. & Larsen, J. J. (2018). Extraction and use of noise models from transient electromagnetic data. Geophysics, 83(1), E37-E46. https://doi.org/10.1190/geo2017-0299.1

Rasmussen, S., Skovgaard Nyboe, N. & Larsen, J. J. (2016). Extraction and Use of Noise Models from Production-Mode Transient Electromagnetic Data. Abstract from AGU Fall Meeting 2016, San Francisco, California, United States. https://agu.confex.com/agu/fm16/meetingapp.cgi/Paper/168217

Ahmadieh Khanesar, M., Kayacan, E., Teshnelab, M. & Kaynak, O. (2011). Extended Kalman Filter Based Learning Algorithm for Type-2 Fuzzy Logic Systems and its Experimental Evaluation. IEEE Transactions on Industrial Electronics, 59(11), 4443-4455. https://doi.org/10.1109/TIE.2011.2151822

Jørgensen, R., Hansen, P. M. & Bro, R. (2006). Exploratory study of winter wheat reflectance during vegetative growth using three-mode component analysis. International Journal of Remote Sensing, 27(5), 919-937.

Mygdalis, V., Iosifidis, A., Tefas, A. & Pitas, I. (2015). Exploiting subclass information in one-class support vector machine for video summarization. In Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on (pp. 2259-2263). IEEE. https://doi.org/10.1109/ICASSP.2015.7178373

Mademlis, I., Iosifidis, A., Tefas, A., Nikolaidis, N. & Pitas, I. (2015). Exploiting Stereoscopic Disparity for Augmenting Human Activity Recognition Performance. Multimedia Tools and Applications, 75(19), 11641-11660. https://doi.org/10.1007/s11042-015-2719-x

Iosifidis, A., Tefas, A. & Pitas, I. (2014). Exploiting Local Class Information in Extreme Learning Machine. In IEEE Symposium Series on Computational Intelligence (SSCI): Computational Intelligence for Engineering Solutions http://poseidon.csd.auth.gr/papers/PUBLISHED/CONFERENCE/pdf/2014/Iosifidis_2014_NCTA.pdf

Kiranyaz, S., Malik, J., Abdallah, H. B., Ince, T., Iosifidis, A. & Gabbouj, M. (2021). Exploiting heterogeneity in operational neural networks by synaptic plasticity. Neural Computing and Applications, 33(13), 7997-8015. https://doi.org/10.1007/s00521-020-05543-w

Cigdem, O., Kayacan, E. & Kaynak, O. (2011). Experimental Study of an Interval Type-2 Fuzzy Neural Network Using Sliding-Mode Online Learning Algorithm. In Control Conference (ASCC), 2011 8th Asian (pp. 1181-1186). IEEE. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5899240

Andersen, K. F., Pham, H. X., Ugurlu, H. I. & Kayacan, E. (2022). Event-based Navigation for Autonomous Drone Racing with Sparse Gated Recurrent Network. In 2022 European Control Conference (ECC) (pp. 1342-1348). IEEE. https://doi.org/10.23919/ECC55457.2022.9838538

Ravindrababu, S., Govdeli, Y., Wei Wong, Z. & Kayacan, E. (2018). Evaluation of the Influence of Build and Print Orientations of Unmanned Aerial Vehicle Parts Fabricated Using Fused Deposition Modeling Process. Journal of Manufacturing Processes, 34(Part A), 659-666. https://doi.org/10.1016/j.jmapro.2018.07.007

Dyrmann, M., Midtiby, H. S. & Nyholm Jørgensen, R. (2016). Evaluation of intra variability between annotators of weed species in color images. Paper presented at International Conference on Agricultural Engineering 2016, Aarhus, Denmark. http://conferences.au.dk/uploads/tx_powermail/cigr2016paper_annotation.pdf

Wagner, S. R., Beevi, F. H. A., Pedersen, C. F., Miranda, J., Cabral, J. & Ravelo, B. (2016). Evaluation of Caregivers' First Time Use of a Seamless Deployment Platform. In Proceedings of the International Conference on E-Health, EH 2016 - Part of the Multi Conference on Computer Science and Information Systems 2016 (pp. 59-66). IADIS.

Grombacher, D., Griffiths, M. P. & Maurya, P. K. (2024). Estimation of transmitter-receiver separation for mobile ground-based transient electromagnetic systems using on-time gates. Abstract from AGU Fall meeting 2024, Washington DC, United States.

Skovsen, S., Dyrmann, M., Mortensen, A. K., Steen, K. A., Green, O., Eriksen, J., Gislum, R., Nyholm Jørgensen, R. & Karstoft, H. (2017). Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks. Sensors, 17(12), Article 2930. https://doi.org/10.3390/s17122930

Nielsen, K. K., Andersen, H. J., Thomsen, A., Jørgensen, J. R. & Jørgensen, R. N. (2009). Estimation of leaf area index in cereal crops using red–green images. Biosystems Engineering, 104(3), 308-317. https://doi.org/10.1016/j.physletb.2003.10.071

Lakshminarayanan, S., Duecker, D., Sarabakha, A., Ganguly, A., Takayama, L. & Haddadin, S. (2024). Estimation of External Force acting on Underwater Robots. In 2024 IEEE 20th International Conference on Automation Science and Engineering, CASE 2024 (pp. 3125-3131). IEEE. https://doi.org/10.1109/CASE59546.2024.10711608

Mortensen, A. K., Gislum, R., Larsen, R. & Nyholm Jørgensen, R. (2015). Estimation of above-ground dry matter and nitrogen uptake in catch crops using images acquired from an octocopter. In J. V. Stafford (Ed.), Precision agriculture '15: Papers presented at the 10th European Conference on Precision Agriculture (Vol. 2015, pp. 127-134). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-814-8_15

Mortensen, A. K., Laursen, M. S. & Nyholm Jørgensen, R. (2018). Estimation of Above-Ground Biomass and Nitrogen-Content of Agricultural Field Crops using Computer Vision. Pictures, Video and sound recordings (digital)

Mortensen, A. K., Karstoft, H., Søegaard, K. & Nyholm Jørgensen, R. (2014). Estimating the content of clover and grass in the sward using a consumer camera and image processing. Abstract from Agromek and NJF joint seminar, Herning, Denmark. http://www.njf.nu/477.htm

Midtiby, H., Mosgaard Giselsson, T. & Jørgensen, R. N. (2012). Estimating plant stem emerging points (PSEPs) of sugar of beets in early growth stages. Biosystems Engineering, 111(1), 83-90. https://doi.org/10.1016/j.biosystemseng.2011.10.011

Jørgensen, R. N., Sørensen, C. G., Green, O. & Kristensen, K. (2009). Estimating impact on clover-grass yield caused by traffic intensities. In C. Lokhorst & P. W. G. Groot Koerkamp (Eds.), Precision livestock farming '09 (Vol. 2009, pp. 143-147). Wageningen Academic Publishers.

Jørgensen, R. N., Sørensen, C. A. G., Green, O. & Kristensen, K. (2010). Estimating impact on clover-grass yield caused by traffic intensities.

Larsen, P. G., Kristiansen, E. L., Bennedsen, J. & Bjerge, K. (2017). Enhancing non-technical skills by a multidisciplinary engineering summer school. European Journal of Engineering Education, 42(6), 1076-1096. https://doi.org/10.1080/03043797.2016.1261278

Iosifidis, A., Tefas, A. & Pitas, I. (2015). Enhancing ELM-based facial image classification by exploiting multiple facial views. In Procedia Computer Science (Vol. 51, pp. 2814-2821). ELSEVIER. https://doi.org/10.1016/j.procs.2015.05.440

Iosifidis, A., Tefas, A. & Pitas, I. (2015). Enhancing Class Discrimination in Kernel Discriminant Analysis. In Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on (pp. 1926-1930). IEEE. https://doi.org/10.1109/ICASSP.2015.7178306

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), Article 4179. https://doi.org/10.3390/en13164179

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

S. Nadimi, E., Blanes-Vidal, V. & Jørgensen, R. N. (2009). ENERGY GENERATION FOR WIRELESS SENSOR NETWORK BASED animal behavior monitoring system.

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

Sohrab, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2020). Ellipsoidal Subspace Support Vector Data Description. IEEE Access, 8, 122013-122025. Article 9133428. https://doi.org/10.1109/ACCESS.2020.3007123

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

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Revised 10.08.2023

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AU Engineering