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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Nyholm Jørgensen, R. (2020). Plantekongres 2020 - RoboWeedMaps præsenterer: Flere muligheder med digital ukrudtsgenkendelse. Pictures, Video and sound recordings (digital) https://www.youtube.com/watch?v=HEsJ9k1SJYM

Imanberdiyev, N. & Kayacan, E. (2020). Redundancy Resolution based Trajectory Generation for Dual-Arm Aerial Manipulators via Online Model Predictive Control. In Proceedings - IECON 2020: 46th Annual Conference of the IEEE Industrial Electronics Society (pp. 674-681). Article 9254702 IEEE. https://doi.org/10.1109/IECON43393.2020.9254702

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. Article 9046850. https://doi.org/10.1109/TTE.2020.2983247

Mehndiratta, M., Kayacan, E., Reyhanoglu, M. & Kayacan, E. (2020). Robust Tracking Control of Aerial Robots Via a Simple Learning Strategy-Based Feedback Linearization. IEEE Access, 8, 1653-1669. https://doi.org/10.1109/ACCESS.2019.2962512

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

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, Article 101215. https://doi.org/10.1016/j.est.2020.101215

Bozcan, I. & Kayacan, E. (2020). UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 1158-1164). Article 9341790 IEEE. https://doi.org/10.1109/IROS45743.2020.9341790

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. Article 9234624. https://doi.org/10.1109/TSP.2020.3029659

Bjerge, K., Frigaard, C. E., Mikkelsen, P. H., Nielsen, T. H., Misbih, M. & Kryger, P. (2019). A computer vision system to monitor the infestation level of Varroa destructor in a honeybee colony. Computers and Electronics in Agriculture, 164(September), Article 104898. https://doi.org/10.1016/j.compag.2019.104898

Mehndiratta, M. & Kayacan, E. (2019). A constrained instantaneous learning approach for aerial package delivery robots: onboard implementation and experimental results. Autonomous Robots, 43(8), 2209-2228. https://doi.org/10.1007/s10514-019-09875-y

Teimouri, N., Christiansen, M. P., Nyholm Jørgensen, R. & Grøn Sørensen, C. (2019). A deep learning-based approach for crop classification using dual-polarimetric C-band radar data.

Imanberdiyev, N. & Kayacan, E. (2019). A fast learning control strategy for unmanned aerial manipulators. Journal of Intelligent and Robotic Systems, 94(3-4), 805-824. https://doi.org/10.1007/s10846-018-0884-7

Naveed, K., Ehsan, S., McDonald-Maier, K. D. & Ur Rehman, N. (2019). A multiscale denoising framework using detection theory with application to images from CMOS/CCD sensors. Sensors (Switzerland), 19(1), Article 206. https://doi.org/10.3390/s19010206

Imanberdiyev, N., Monica, J. & Kayacan, E. (2019). A multi-task velocity-based redundancy resolution strategy for unmanned aerial manipulators. In 2019 18th European Control Conference, ECC 2019 (pp. 1130-1135). Article 8796272 IEEE. https://doi.org/10.23919/ECC.2019.8796272

Khawaja, A., Khan, T. M., Naveed, K., Naqvi, S. S., Rehman, N. U. & Nawaz, S. J. (2019). An Improved Retinal Vessel Segmentation Framework Using Frangi Filter Coupled With the Probabilistic Patch Based Denoiser. IEEE Access, 7, 164344-164361. https://doi.org/10.1109/access.2019.2953259

Teimouri, N., Dyrmann, M. & Nyholm Jørgensen, R. (2019). A novel spatio-temporal FCN-LSTM Network for Recognizing Various Crop Types Using Multi-Temporal Radar Images. Remote Sensing, 11(8), Article 990. https://doi.org/10.3390/rs11080990

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

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.

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

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

Kragh, M. F., Rimestad, J., Berntsen, J. & Karstoft, H. (2019). Automatic grading of human blastocysts from time-lapse imaging. Computers in Biology and Medicine, 115, Article 103494. https://doi.org/10.1016/j.compbiomed.2019.103494

Kragh, M. F., Rimestad, J., Berntsen, J. & Karstoft, H. (2019). Automatic morphological grading of human blastocysts with time-lapse imaging and artificial intelligence. Abstract from ESHRE, Vienna, Austria. https://www.eshre.eu/ESHRE2019/Programme/Searchable#!abstractdetails/0000573820

Gislum, R., Nyholm Jørgensen, R., Thomsen, I. K., Hansen, E. M. & Olesen, J. E., (2019). Beskrivelse af setup for vidensindsamling og måling af NDVI-værdi i pilotprojekt om biomasse, No. 2019-760-001283, 3 p., Jul 03, 2019.

Liu, L., Grombacher, D. J., Auken, E. & Larsen, J. J. (2019). Complex envelope retrieval for surface nuclear magnetic resonance data using spectral analysis. Geophysical Journal International, 217(2), 894-905. https://doi.org/10.1093/gji/ggz068

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

Sørensen, R. A., Nielsen, M. & Karstoft, H. (2019). Deep Reinforcement Learning for Route Optimization in Baggage Handling Systems. In S. Y. Yurish (Ed.), Advances in Signal Processing and Artificial Intelligence: Proceedings of the 1st International Conference on Advances in Signal Processing and Artifical Intelligence (pp. 29-33). Article 11 IFSA Publishing.

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.

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

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

Malik, Q. W., Rehman, N. U., Gull, S., Ehsan, S. & McDonald-Maier, K. D. (2019). FPGA-Based Real-Time Implementation of Bivariate Empirical Mode Decomposition. Circuits, Systems, and Signal Processing, 38(1), 118-137. https://doi.org/10.1007/s00034-018-0844-2

Gul, S., Siddiqui, M. F. & Rehman, N. U. (2019). FPGA Based Real-Time Implementation of Online EMD With Fixed Point Architecture. IEEE Access, 7, 176565-176577. https://doi.org/10.1109/access.2019.2957819

Madsen, S. L., Dyrmann, M., Nyholm Jørgensen, R. & Karstoft, H. (2019). Generating artificial images of plant seedlings using generative adversarial networks. Biosystems Engineering, 187, 147-159. https://doi.org/10.1016/j.biosystemseng.2019.09.005

Sarabakha, A., Fu, C. & Kayacan, E. (2019). Intuit before tuning: Type-1 and type-2 fuzzy logic controllers. Applied Soft Computing Journal, 81, Article 105495. https://doi.org/10.1016/j.asoc.2019.105495

Zhou, S., Helwa, M. K., Schoellig, A. P., Sarabakha, A. & Kayacan, E. (2019). Knowledge transfer between robots with similar dynamics for high-accuracy impromptu trajectory tracking. In 2019 18th European Control Conference, ECC 2019 Article 8796140 IEEE. https://doi.org/10.23919/ECC.2019.8796140

Mehndiratta, M., Kayacan, E., Patel, S., Kayacan, E. & Chowdhary, G. (2019). Learning-based Fast Nonlinear Model Predictive Control for Custom-made 3D Printed Ground and Aerial Robots. In S. V. Rakovic & W. Levine (Eds.), Handbook of Model Predictive Control (1 ed., pp. 581-605). Birkhäuser Verlag. https://doi.org/10.1007/978-3-319-77489-3_24

Govdeli, Y., Bin Muzaffar, S. M., Raj, R., Elhadidi, B. & Kayacan, E. (2019). Learning Control of Tandem-Wing Tilt-Rotor UAV with Unsteady Aerodynamic Model. In 2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019 Article 8859023 IEEE. https://doi.org/10.1109/FUZZ-IEEE.2019.8859023

Camci, E. & Kayacan, E. (2019). Learning motion primitives for planning swift maneuvers of quadrotor. Autonomous Robots, 43(7), 1733-1745. https://doi.org/10.1007/s10514-019-09831-w

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

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

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.

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

Naveed, K., Shaukat, B., Ehsan, S., Mcdonald-Maier, K. D., Rehman, N. U. & Baghaie, A. (Ed.) (2019). Multiscale image denoising using goodness-of-fit test based on EDF statistics. PLoS One, 14(5), e0216197. https://doi.org/10.1371/journal.pone.0216197

Rehman, N. U. & Aftab, H. (2019). Multivariate Variational Mode Decomposition. IEEE Transactions on Signal Processing, 67(23), 6039. Article 8890883. https://doi.org/10.1109/TSP.2019.2951223

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. In O. Huguenin-Elie, B. Studer, R. Kölliker, D. Reheul, M. Probo, P. Barre, U. Feuerstein, I. Roldan-Ruiz, P. Mariotte & A. Hopkins (Eds.), 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 (pp. 103-109). Wageningen Academic Publishers.

Ma, X., Hegarty, P. J., Jørgensen, K. F. & Larsen, J. J. (2019). Nonlinear distortion reduction in sound zones by constraining individual loudspeaker control effort. Journal of the Audio Engineering Society, 67(9), 641-654. https://doi.org/10.17743/jaes.2019.0015

Sarabakha, A. & Kayacan, E. (2019). Online deep learning for improved trajectory tracking of unmanned aerial vehicles using expert knowledge. In 2019 International Conference on Robotics and Automation, ICRA 2019 (pp. 7727-7733). Article 8794314 IEEE. https://doi.org/10.1109/ICRA.2019.8794314

Camci, E. & Kayacan, E. (2019). Planning swift maneuvers of quadcopter using motion primitives explored by reinforcement learning. In Proceedings of the American Control Conference: 2019 American Control Conference, ACC 2019 (pp. 279-285). Article 8815352 IEEE.

Christiansen, M. P., Teimouri, N., Laursen, M. S., Mikkelsen, B. F., Jorgensen, R. N. & Sorensen, C. A. G. (2019). Preprocessed sentinel-1 data via a web service focused on agricultural field monitoring. IEEE Access, 7(1), 65139-65149. Article 8715769. https://doi.org/10.1109/ACCESS.2019.2917063

Kremer, T., Juul Larsen, J. & Nguyen, F. (2019). Processing harmonic EM noise with multiple or unstable frequency content in surface NMR surveys. Geophysical Journal International, 219(2), 753-775. https://doi.org/10.1093/gji/ggz307

Yan, R. J., Kayacan, E., Chen, I. M., Tiong, L. K. & Wu, J. (2019). QuicaBot: Quality Inspection and Assessment Robot. IEEE Transactions on Automation Science and Engineering, 16(2), 506-517. https://doi.org/10.1109/TASE.2018.2829927

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Revised 10.08.2023

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