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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Grombacher, D., Liu, L., Griffiths, M. P., Vang, M. & Larsen, J. J. (2021). Steady-State Surface NMR for Mapping of Groundwater. Geophysical Research Letters, 48(23), Article e2021GL095381. https://doi.org/10.1029/2021GL095381

Laursen, M. S., Midtiby, H. S., Kruger, N. & Jorgensen, R. N. (2014). Statistics-based segmentation using a continuous-scale naive Bayes approach. Computers and Electronics in Agriculture, 109, 271-277. https://doi.org/10.1016/j.compag.2014.10.009

Kayacan, E., Ahmadieh Khanesar, M. & Kayacan, E. (2015). Stabilization of Type-2 Fuzzy Takagi- Sugeno-Kang Identifier Using Lyapunov Functions. In Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on (pp. 1-6). IEEE . https://doi.org/10.1109/FUZZ-IEEE.2015.7337809

Laursen, M. S., Midtiby, H., Krüger, N. & Jørgensen, R. N. (2012). Spray boom for selectively spraying a herbicidal composition onto dicots. (Patent No. WO 2012/122988 A1).

Chumachenko, K., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2021). Speed-up and Multi-view Extensions to Subclass Discriminant Analysis. Pattern Recognition, 111, Article 107660. https://doi.org/10.1016/j.patcog.2020.107660

Soltanian, M., Malik, J., Raitoharju, J., Iosifidis, A., Kiranyaz, S. & Gabbouj, M. (2021). Speech Command Recognition in Computationally Constrained Environments with a Quadratic Self-Organized Operational Layer. In 2021 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/IJCNN52387.2021.9534232

Jørgensen, R., Christensen, L. K. & Bro, R. (2007). Spectral reflectance at sub-leaf scale including the spatial distribution discriminating NPK stress characteristics in barley using multiway partial least squares regression. International Journal of Remote Sensing, 28(5), 943-962. https://doi.org/10.1080/01431160600735657

Christensen, L. K. & Jørgensen, R. (2004). Spectral NPK stress discrimination in barley plants by use of three wavebands. In Proc. 4th int. Crop Science Congress, Brisbane, Queensland, Australia. Handbook and Abstracts, Section 2.5.3., p. 195. http://www.regional.org.au/au/cs/2004/index.htm

Hansen, O. L. P., Svenning, J. C., Olsen, K., Dupont, S., Garner, B. H., Iosifidis, A., Price, B. W. & Høye, T. T. (2020). Species-level image classification with convolutional neural network enables insect identification from habitus images. Ecology and Evolution, 10(2), 737-747. https://doi.org/10.1002/ece3.5921

Skovsen, S., Laursen, M. S., Gislum, R., Eriksen, J., Dyrmann, M., Mortensen, A. K., Farkhani, S., Karstoft, H., Jensen, N.-P. & Nyholm Jørgensen, R. (2019). Species distribution mapping of grass clover leys using images for targeted nitrogen fertilization. In J. V. Stafford (Ed.), Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019 (pp. 639-645). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-888-9_79

Rydahl, P., Bojer, O. M., Jorgensen, R. N., Dyrmann, M., Andersen, P., Jensen, N. & Sorensen, M. (2018). Spatial variability of optimized herbicide mixtures and dosages. In Proceedings 14th International Conference on Precision Agriculture (ICPA2018) (pp. 1-14). Article 5040 International Society of Precision Agriculture. https://www.ispag.org/proceedings/?action=abstractid=5040

Christensen, L. K. & Jørgensen, R. (2003). Spatial reflectance at sub-leaf scale discriminating NPK stress in barley. In Proc. 4th ECPA and 1st ECPLF, Berlin, Germany (pp. 622-622). Wageningen Academic Publishers.

Christensen, L. K. & Jørgensen, R. (2003). Spatial Reflectance at Sub-Leaf Scale Discriminating NPK Stress Characteristics in Barley Using Multiway Partial Least Squares Regression. Paper presented at ASAE Annual International Meeting, Las Vegas, United States. http://asae.frymulti.com/request.asp?JID=5&AID=13744&CID=lnv2003&T=2

Farkhani, S., Kragh, M. F., Christiansen, P. H., Nyholm Jørgensen, R. & Karstoft, H. (2019). Sparse-to-Dense Depth Completion in Precision Farming. In 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 Article 35 Association for Computing Machinery. https://doi.org/10.1145/3387168.3387230

Pedersen, C. F. & Bäckström, T. (2014). Sparse Time-Frequency Representation of Speech by the Vandermonde Transform. In Proceedings of the Annual Conference of the International Speech Communication Association: INTERSPEECH 2014 (pp. 2248-2252)

Iosifidis, A., Tefas, A. & Pitas, I. (2015). Sparse Extreme Learning Machine classifier exploiting Intrinsic Graphs. Pattern Recognition Letters, 65, 192-196. https://doi.org/10.1016/j.patrec.2015.07.036

Jensen, T., Munkholm, L. J., Green , O. & Karstoft, H. (2015). Soil Surface Roughness Using Cumulated Gaussian Curvature. In L. Nalpantidis, V. Krüger, J.-O. Eklundh & A. Gasteratos (Eds.), Computer Vision Systems : 10th International Conference, ICVS 2015, Proceedings (pp. 533-541). Springer. https://doi.org/10.1007/978-3-319-20904-3_48

Ahmadieh Khanesar, M., Kayacan, E., Kaynak, O. & Saeys, W. (2013). Sliding Mode Type-2 Fuzzy Control of Robotic Arm Using Ellipsoidal Membership Functions. In Control Conference (ASCC), 2013 9th Asian (pp. 1-6). IEEE. https://doi.org/10.1109/ASCC.2013.6606393

Kayacan, E. & Kaynak, O. (2012). Sliding Mode Control Theory-based Algorithm for Online Learning in Type-2 Fuzzy Neural Networks: Application to Velocity Control of an Electro Hydraulic Servo System. INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 26(7), 645-659. https://doi.org/10.1002/acs.1292

Rubio Hervas, J., Kayacan, E., Reyhanoglu, M. & Tang, H. (2014). Sliding Mode Control of Fixed- Wing UAVs in Windy Environments. In Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on (pp. 986-991). IEEE . https://doi.org/10.1109/ICARCV.2014.7064440

Kayacan, E., Cigdem, O. & Kaynak, O. (2011). Sliding Mode Control Approach for Online Learning as Applied to Type-2 Fuzzy Neural Networks and its Experimental Evaluation. IEEE Transactions on Industrial Electronics, 59(9), 3510-3520. https://doi.org/10.1109/TIE.2011.2182017

Lati, R. N., Rasmussen, J., Andujar, D., Dorado, J., Berge, T. W., Wellhausen, C., Pflanz, M., Nordmeyer, H., Schirrmann, M., Eizenberg, H., Neve, P., Jørgensen, R. N. & Christensen, S. (2021). Site-specific weed management—constraints and opportunities for the weed research community: Insights from a workshop. Weed Research, 61(3), 147-153. https://doi.org/10.1111/wre.12469

Kayacan, E. & Kaynak, O. (2011). Single-step Ahead Prediction Based on the Principle of Concatenation Using Grey Predictors. Expert Systems with Applications, 38(8), 9499-9505. https://doi.org/10.1016/j.eswa.2011.01.143

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2022). Single-layer vision transformers for more accurate early exits with less overhead. Neural Networks, 153, 461-473. https://doi.org/10.1016/j.neunet.2022.06.038

Iosifidis, A., Tefas, A. & Pitas, I. (2015). Single-hidden Layer Feedforward Neural network training using class geometric information. In Lecture Notes: Studies in Computational Intelligence Springer.

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

Cros, O., Volden, M., Flaaris, J. J., Brix, L. C., Pedersen, C. F., Hansen, K. V., Larsen, O. V., Østergaard, L. R. & Haase, J. (2002). Simulating the puncture of the human ventricle. In 10th Annual Medicine Meets Virtual Reality (MMVR) Conference, Digital upgrades: Applying Moore's Law to health

Oniz, Y., Kayacan, E. & Kaynak, O. (2008). Simulated and Experimental Study of Antilock Braking System Using Grey Sliding Mode Control. In Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on (pp. 90-95). IEEE . https://doi.org/10.1109/ICSMC.2007.4413975

Ugurlu, H. I., Pham, X. H. & Kayacan, E. (2022). Sim-to-Real Deep Reinforcement Learning for Safe End-to-End Planning of Aerial Robots. Robotics, 11(5), Article 109. https://doi.org/10.3390/robotics11050109

Fu, C., Sarabakha, A., Kayacan, E., Wagner, C., John, R. & Garibaldi, J. M. (2017). Similarity-based Non-Singleton Fuzzy Logic Control for Improved Uncertainty Handling in Quadrotor UAVs. In Fuzzy Systems (FUZZ-IEEE), 2017 IEEE International Conference on (pp. 1-6). IEEE. https://doi.org/10.1109/FUZZ-IEEE.2017.8015440

Nielsen, L., Olesen, C. R., Nyholm Jørgensen, R. & Fris, H. J. (2015). Sikring af levesteder på engarealer med biomassehøst i kombination med vildthensyn. Poster session presented at Biodiversitetssymposiet 2015, Aarhus , Denmark.

Nielsen, L., Olesen, C. R., Nyholm Jørgensen, R. & Fris, H. J. (2015). Sikring af levesteder på engarealer med biomassehøst i kombination med vildthensyn. Abstract from Biodiversitetssymposiet 2015, Aarhus , Denmark.

Liu, L., Grombacher, D., Griffiths, M., Vang, M. & Larsen, J. J. (2023). Signal Processing Steady-State Surface NMR Data. IEEE Transactions on Instrumentation and Measurement, 72, Article 6502313. https://doi.org/10.1109/TIM.2023.3264033

Naveed, K., Shaukat, B. & Rehman, N. U. (2017). Signal denoising based on dual tree complex wavelet transform and goodness of fit test. In 2017 22nd International Conference on Digital Signal Processing (DSP) IEEE. https://doi.org/10.1109/icdsp.2017.8096067

Larsen, J. J. & Vienne, G. (2004). Side pumping of double-clad photonic crystal fibers. Optics Letters, 29(5), 436-438. https://doi.org/10.1364/OL.29.000436

Pedersen, C. F., Andersen, O. & Dalsgaard, P. (2010). Separation of Mixed Phase Signals by Zeros of the Z-transform: A Reformulation of Complex Cepstrum Based Separation by Causality. I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings, 2010(35), 5050-5053. https://doi.org/10.1109/ICASSP.2010.5495060

Skjødt, P., Hansen, P. M., Jørgensen, R. & Nielsen, N. E. (2003). Sensor based nitrogen fertilization in winter wheat levelling out spatial variability in protein content. In Proc. 4th ECPA and 1st ECPLF, Berlin, Germany (pp. 311-311). Wageningen Academic Publishers.

Skjødt, P. & Jørgensen, R. (2003). Sensor based nitrogen fertilization increasing grain protein yield in winter wheat. Risø Report Vol. Risø-R-1389(EN)

Streibig, J. C., Rasmussen, J., Andujar, D., Andreasen, C., Berge, T. W., Chachalis, D., Dittmann, T., Gerhards, R., Giselsson, T. M., Hamouz, P., Jaeger-Hansen, C., Jensen, K., Nyholm Jørgensen, R., Keller, M., Laursen, M., Midtiby, H. S., Nielsen, J., Müller, S., Nordmeyer, H. ... Christensen, S. (2014). Sensor-based assessment of herbicide effects. Weed Research, 54(3), 223–233. https://doi.org/10.1111/wre.12079

Christensen, S., Dyrmann, M., Nyholm Jørgensen, R. & Laursen, M. S. (2021). Sensing for Weed Detection. In Sensing Approaches for Precision Agriculture (pp. 275-300). Springer. https://doi.org/10.1007/978-3-030-78431-7, https://doi.org/10.1007/978-3-030-78431-7_10

Gomes, C., Lucani Rötter, D. E., Iosifidis, A., Feng, H., Ejersbo, H. & Frasheri, M. (2024). Sensing and Communication of Data from the Physical Twin. In J. Fitzgerald, C. Gomes & P. G. Larsen (Eds.), The Engineering of Digital Twins (pp. 147-171). Springer. https://doi.org/10.1007/978-3-031-66719-0_7#citeas

Mygdalis, V., Iosifidis, A., Tefas, A. & Pitas, I. (2018). Semi-Supervised Subclass Support Vector Data Description for Image and Video Classification. Neurocomputing, 278, 51-61. https://doi.org/10.1016/j.neucom.2017.08.028

Iosifidis, A., Tefas, A. & Pitas, I. (2014). Semi-supervised classification of human actions based on Neural Networks. In Pattern Recognition (ICPR), 2014 22nd International Conference on (pp. 1336-1341). IEEE. https://doi.org/10.1109/ICPR.2014.239

Mortensen, A. K., Dyrmann, M., Karstoft, H., Nyholm Jørgensen, R. & Gislum, R. (2016). Semantic Segmentation of Mixed Crops using Deep Convolutional Neural Network. Paper presented at CIGR 2016, World Congress. http://conferences.au.dk/uploads/tx_powermail/biomap_-_cigr_2016_-_paper_-_final.pdf

Larsen, D., Steen, K. A., Skovsen, S., Grooters, K., Eriksen, J., Nyholm Jørgensen, R., Dyrmann, M. & Green, O. (2018). Semantic Segmentation of Clover-Grass Images using Images from Commercially Available Drones. In P. W. G. Groot Koerkamp, C. Lokhorst , A. H. Ipema, C. Kempenaar, C. M. Groenestein, C. G. van Oostrum & N. J. Ros (Eds.), Book of Abstracts of the European Conference on Agricultural Engineering: AgEng2018 (pp. 110). Wageningen University. https://doi.org/10.18174/471678

Cavagna, A., Li, N., Iosifidis, A. & Zhang, Q. (2023). Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications. In 2023 IEEE International Conference on Communications Workshops (ICC Workshops) (pp. 1617-1622). IEEE. https://doi.org/10.1109/ICCWorkshops57953.2023.10283786

Chen, Q., Chen, J., Lang, X., Xie, L., Rehman, N. U. & Su, H. (2021). Self-tuning variational mode decomposition. Journal of the Franklin Institute, 358(15), 7825-7862. https://doi.org/10.1016/j.jfranklin.2021.07.021

Hansen, M. K., Underwood, J. & Karstoft, H. (2016). Self-supervised Traversability Assessment in Field Environments with Lidar and Camera. Poster session presented at International Conference on Agricultural Engineering 2016, Aarhus, Denmark.

Kiranyaz, S., Malik, J., Abdallah, H. B., Ince, T., Iosifidis, A. & Gabbouj, M. (2021). Self-organized Operational Neural Networks with Generative Neurons. Neural Networks, 140, 294-308. https://doi.org/10.1016/j.neunet.2021.02.028

Zahra, A., Kanwal, N., Rehman, N. U., Ehsan, S. & McDonald-Maier, K. D. (2017). Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition. Computers in Biology and Medicine, 88, 132-141. https://doi.org/10.1016/j.compbiomed.2017.07.010

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