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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Farsizadeh, H., Gheisarnejad, M., Mosayebi, M., Rafiei Foroushani, M. & Khooban, M. H. (2020). An Intelligent and Fast Controller for DC/DC Converter Feeding CPL in a DC Microgrid. IEEE Transactions on Circuits and Systems II: Express Briefs, 67(6), 1104-1108. Article 8764402. https://doi.org/10.1109/TCSII.2019.2928814

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

Christensen, L. T. B., Straadt, D., Vassis, S., Lillelund, C. M., Stoustrup, P. B., Pauwels, R., Pedersen, T. K. & Pedersen, C. F. (2024). An Explainable and Conformal AI Model to Detect Temporomandibular Joint Involvement in Children Suffering from Juvenile Idiopathic Arthritis. (pp. 1-4). https://arxiv.org/abs/2405.01617#

Todnem Bach Christensen, L., Straadt, D., Vassis, S., Lillelund, C. M., Stoustrup, P. B., Pauwels, R., Pedersen, T. K. & Pedersen, C. F. (2024). An Explainable and Conformal AI Model to Detect Temporomandibular Joint Involvement in Children Suffering from Juvenile Idiopathic Arthritis. In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA (pp. 1-4). IEEE. https://doi.org/10.1109/EMBC53108.2024.10781771

Prach, A., Kayacan, E. & S. Bernstein, D. (2016). An Experimental Evaluation of the Forward Propagating Riccati Equation to Nonlinear Control of the Quanser 3 DOF Hover Testbed. In 2016 American Control Conference, ACC 2016 (pp. 3710-3715). Article 7525490 IEEE. https://doi.org/10.1109/ACC.2016.7525490

Beevi, F. H. A., Miranda, J., Pedersen, C. F. & Wagner, S. R. (2015). An Evaluation of Commercial Pedometers for Monitoring Slow Walking Speed Populations. Telemedicine and e-Health, 22(5). https://doi.org/10.1089/tmj.2015.0120

Asif, M. R., Bording, T. S., Maurya, P. K., Zhang, B., Fiandaca, G., Grombacher, D. J., Christiansen, A. V., Auken, E. & Larsen, J. J. (2022). A Neural Network-Based Hybrid Framework for Least-Squares Inversion of Transient Electromagnetic Data. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 4503610. https://doi.org/10.1109/TGRS.2021.3076121

Ahmadi, H., Rafiei Foroushani, M., Afshari-Igder, M., Gheisarnejad Chirani, M. & Khooban, M. H. (2021). An Energy Efficient Solution for Fuel Cell Heat Recovery in Zero-Emission Ferry Boats: Deep Deterministic Policy Gradient. I E E E Transactions on Vehicular Technology, 70(8), 7571-7581. Article 9477203. https://doi.org/10.1109/TVT.2021.3094899.

Letafat, A., Rafiei, M., Ardeshiri, M., Sheikh, M., Banaei, M., Boudjadar, J. & Khooban, M. H. (2020). An Efficient and Cost-Effective Power Scheduling in Zero-Emission Ferry Ships. Complexity, 2020, Article 6487873. https://doi.org/10.1155/2020/6487873

Andersen, B. H., Kristensen, E. F., Jensen, H. F., Persson, A., Jørgensen, R., Sørensen, C. A. G., Jensen, K. & Pedersen, J. M. (2007). An autonomous mobile robot for feeding outdoor pigs. In Ikke angivet (pp. 289-291). NJF, Nordic Association of Agricultural Scientists.

Bjerge, K., Nielsen, J. B., Sepstrup, M. V., Helsing-Nielsen, F. & Høye, T. T. (2021). An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning. Sensors (Switzerland), 21(2), Article 343. https://doi.org/10.3390/s21020343

Steen, K. A., Therkildsen, O. R., Karstoft, H. & Green, O. (2013). An Audio Based Adaptive Goose Scaring Device. Paper presented at CIOSTA XXXV Conference, Billund, Denmark.

Grombacher, D., Kass, M. A., Auken, E. & Larsen, J. J. (2020). An approximate fast-mapping approach to the surface NMR forward problem. Geophysical Journal International, 221(2), 928-937. https://doi.org/10.1093/gji/ggaa044

Oleksiienko, I. & Iosifidis, A. (2021). Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems. In N. Mallenahalli, A. Bhattacharya, S. Senatore, A. Negi & A. Hirose (Eds.), 2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) (pp. 59-64). IEEE. https://doi.org/10.1109/ICETCI51973.2021.9574075

Kristensen, K. & Jørgensen, R. N. (2007). Analysis of trials with many replicates where treatments are randomised into rows and blocks. In H. Bleiholder & H.-P. Piepgo (Eds.), Proceedings of the International Symposium (pp. 92-97). University of Hohenheim.

Ahmadieh Khanesar, M., Kayacan, E., Teshnelab, M. & Kaynak, O. (2011). Analysis of the Noise Reduction Property of Type-2 Fuzzy Logic Systems Using a Novel Type-2 Membership Function. IEEE Transactions on Cybernetics, 41(5), 1395-1406. https://doi.org/10.1109/TSMCB.2011.2148173

Khan, N., Baig, F., Nawaz, S., Rehman, N. U. & Sharma, S. (2016). Analysis of Power Quality Signals Using an Adaptive Time-Frequency Distribution. Energies, 9(11), 933. https://doi.org/10.3390/en9110933

Miranda, J., Cabral, J., Banerjee, S., Grossmann, D., Pedersen, C. F. & Wagner, S. R. (2017). Analysis of OPC Unified Architecture for Healthcare Applications. In 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2017: EFTA 2017 (pp. 1-4). IEEE. https://doi.org/10.1109/ETFA.2017.8247771

Ravelo, B., Miranda, J., Cabral, J., Wagner, S. R., Pedersen, C. F., Memon, M., Mathiesen, M. & Jastrzebski , A. (2015). Analysis of a Healthcare Platform RF Emission in Indoor Environment. In Proceedings of the IEEE International Symposium on Electromagnetic Compatibility and EMC Europe: EMC 2015 (pp. 1495-1500). IEEE.

Pedersen, C. F., Dalsgaard, P., Larsen, L. B., Jensen, J. J., Hejlesen, O. K., Sørensen, L. T., Schultz, N. & Christensen, D. S. (2004). Analysis and design of a PN based health care service for diabetics. Paper presented at My Personal Adaptive Global NET: Visions and beyond, Shanghai, China.

Somerville, G. J., Mathiassen, S. K., Melander, B., Bøjer, O. M. & Nyholm Jørgensen, R. (2021). Analysing the number of images needed to create robust variable spray maps. Precision Agriculture, 22(5), 1377-1396. https://doi.org/10.1007/s11119-021-09800-3

Lindahl Petersen , K., Ladegaard Jensen, K., Back Nielsen, M., Pas, L.-C., Jensen, N.-P., Nielsen, P. R., Bøjer, O. M., Nyholm Jørgensen, R., Laursen, M. S., Teimouri, N. & Hartmann, B. (2021). Analyse af mulige herbicidbesparelser ved brug af erfaringer og data fra RoboWeedMaPS. Datalogisk Institut. https://datalogisk.dk/wp-content/uploads/2021/03/MST_20rapport_version_2_2002092021.pdf

Sarabakha, A. & Suganthan, P. N. (2023). anafi_ros: from Off-the-Shelf Drones to Research Platforms. In 2023 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 1308-1315)

Camci, E., Raju Kripalani, D., Ma, L., Kayacan, E. & Ahmadieh Khanesar, M. (2018). An Aerial Robot for Rice Farm Quality Inspection With Type-2 Fuzzy Neural Networks Tuned by Particle Swarm Optimization-Sliding Mode Control Hybrid Algorithm. Swarm and Evolutionary Computation, 41, 1-8. https://doi.org/10.1016/j.swevo.2017.10.003

Steen, K. A., Therkildsen, O. R., Karstoft, H. & Green, O. (2015). An adaptive scaring device. International Journal of Sustainable Agricultural Management and Informatics, 1(2), 130-141. https://doi.org/10.1504/IJSAMI.2015.070746

Cisel Aras, A., Kayacan, E., Oniz, Y., Kaynak, O. & H. Abiyev, R. (2010). An Adaptive Neuro-Fuzzy Architecture for Intelligent Control of a Servo System and its Experimental Evaluation. In Industrial Electronics (ISIE), 2010 IEEE International Symposium on (pp. 68-73). IEEE . https://doi.org/10.1109/ISIE.2010.5637706

Kayacan, E. & Kaynak, O. (2009). An Adaptive Grey PID Type Fuzzy Controller Design for a Nonlinear Liquid Level System. Transactions of the Institute of Measurement and Control, 31(1), 33-49. https://doi.org/10.1177/0142331208090629

Kayacan, E. & Kaynak, O. (2006). An Adaptive Grey Fuzzy PID Controller With Variable Prediction Horizon. In SCIS&ISIS2006 (pp. 760-765) http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=F41AB2B31754039859E731BD9BA145A3?doi=10.1.1.162.1218&rep=rep1&type=pdf

Raza, M. A., Qi, C., Asif, M. R. & Khan, M. A. (2020). An adaptive approach for multi-national vehicle license plate recognition using multi-level deep features and foreground polarity detection model. Applied Sciences, 10(6), Article 2165. https://doi.org/10.3390/app10062165

Abdullah, S. M. U., Rehman, N. U., Khan, M. M. & Mandic, D. P. (2015). A Multivariate Empirical Mode DecompositionBased Approach to Pansharpening. IEEE Transactions on Geoscience and Remote Sensing, 53(7), 3974-3984. https://doi.org/10.1109/tgrs.2015.2388497

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

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

Steen, K. A., Karstoft, H. & Green, O. (2011). A Multimedia Capture System for Wildlife Studies. Paper presented at The Third International Conference on Emerging Network Intelligence, Lissabon, Portugal. http://www.thinkmind.org/index.php?view=article&articleid=emerging_2011_6_50_40032

Liu, L., Grombacher, D. J., Auken, E. & Larsen, J. J. (2017). A Multichannel, Low Noise Surface NMR Receiver System with Wireless Connections to Signal and Reference Coils. In 23rd European Meeting of Environmental and Engineering Geophysics European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.201702012

Jacobsen, R. H., Hansen, F. O., Madsen, J. K., Karstoft, H., Mikkelsen, P. H., Skogberg, T. A., Rasmussen, E. S., Andersen, C., Alrøe, M. & Toftegaard, T. S. (2011). A Modular Platform for Wireless Body Area Network Research and Real-life Experiments: The ASE-BAN Testbed. International Journal on Advances in Networks and Services, 4(3-4), 257-277. http://www.iariajournals.org/networks_and_services/netser_v4_n34_2011_paged.pdf

Jensen, T., Munkholm, L. J., Green, O. & Karstoft, H. (2014). A mobile surface scanner for soil studies. In Proceedings of the Second International Conference on Robotics, Associated High-Technologies and Equipment for Agriculture and Forestry - RHEA 2014: New trends in mobile robotics, perception and actuation for agriculture and forestry (pp. 187-194)

Rysgaard, S., Bjerge, K., Boone, W., Frandsen, E., Graversen, M., Thomas Høye, T., Jensen, B., Johnen, G., Antoni Jackowicz-Korczynski, M., Taylor Kerby, J., Kortegaard, S., Mastepanov, M., Melvad, C., Schmidt Mikkelsen, P., Mortensen, K., Nørgaard, C., Poulsen, E., Riis, T., Sørensen, L. & Røjle Christensen, T. (2022). A mobile observatory powered by sun and wind for near real time measurements of atmospheric, glacial, terrestrial, limnic and coastal oceanic conditions in remote off-grid areas. HardwareX, 12, Article e00331. https://doi.org/10.1016/j.ohx.2022.e00331

Blackmore, B. S., Fountas, S., Vougioukas, S., Tang, L., Sørensen, C. A. G. & Jørgensen, R. (2004). A method to define agricultural robot behaviours. In Proc. int. conf. on Mechatronics & Robotics (MECHROB) 2004, Aachen, Germany. Vol. III (pp. 1197-1200)

Haahr, V., Jørgensen, R., Jensen, A. & Overgaard, J. (1999). A method for optimal site-specific nitrogen application. In Stafford, J.V. (ed.) Precision agriculture '99, Proc. 2nd European conf. on Precision Agriculture, Odense, Denmark, Part 2 (pp. 709-718)

Memon, M., Wagner, S. R., Pedersen, C. F., Beevi, F. H. A. & Hansen, F. O. (2014). Ambient Assisted Living Healthcare Frameworks, Platforms, Standards, and Quality Attributes. Sensors, 14, 4312-4341. https://doi.org/10.3390/s140304312

Memon, M., Wagner, S. R., Hansen, F. O., Pedersen, C. F., Beevi, F. H. A., Mathiesen, M., Nielsen, C. & Langvad, O. (2013). Ambient Assisted Living Ecosystems of Personal Healthcare Systems, Applications, and Devices. In Proceedings - Scandinavian Conference on Health Informatics 2013 (Vol. 91/13, pp. 61-65). Linköping University Electronic Press. http://www.ep.liu.se/ecp/091/013/ecp13091013.pdf

Kanstrup, A. M., Bjerge, K. & Kristensen, J. E. (2010). A Living Laboratory Exploring Mobile Support for Everyday Life with Diabetes. Wireless Personal Communications, 53(3), 395-408. https://doi.org/10.1007/s11277-010-9953-3

Bjerge, K., Nielsen, J. B., Sepstrup, M. V., Helsing-Nielsen, F. & Høye, T. T. (2020). A light trap and computer vision system to detect and classify live moths(Lepidoptera) using tracking and deep learning. Bioarxiv

Hansen, P. M., Skjødt, P. & Jørgensen, R. (2003). Algorithm for variable nitrogen rate - application in winter wheat. In Proc. NJF Seminar 336 on Implementation of Precision Farming in Practical Agriculture. Skara, Sweden, June 2002. DIAS report - Plant Production 100 (pp. 56-64)

Hao, H., Wang, H. L. & Rehman, N. U. (2017). A joint framework for multivariate signal denoising using multivariate empirical mode decomposition. Signal Processing, 135, 263-273. https://doi.org/10.1016/j.sigpro.2017.01.022

Gaikwad, N., Liu, L., Griffiths, M. P., Grombacher, D. & Larsen, J. J. (2023). A High Duty Cycle Transmitter Unit for Steady-State Surface NMR Instruments. (pp. 1-22). Geoscientific Instrumentation, Methods and Data Systems https://doi.org/10.5194/gi-2023-5

Christiansen, M. P., Larsen, P. G. & Nyholm Jørgensen, R. (2016). Agricultural robotic candidate overview using co-model driven development. In A. G. Bruzzone, S. Junco, G. Dauphin-Tanguy & Y. Merkuryev (Eds.), 9th International Conference on Integrated Modeling and Analysis in Applied Control and Automation, IMAACA 2016: IMAACA 2016 (pp. 41-48). Article IMAACA_10 Dime University of Genoa. https://www.msc-les.org/proceedings/imaaca/2016/IMAACA2016_41.pdf

Kayacan, E., Oniz, Y. & Kaynak, O. (2009). A Grey System Modeling Approach for Sliding-Mode Control of Antilock Braking System. IEEE Transactions on Industrial Electronics, 56(8), 3244-3252. https://doi.org/10.1109/TIE.2009.2023098

Oniz, Y., Kayacan, E. & Kaynak, O. (2007). A Grey Sliding Mode Controller Design for Antilock Braking System. In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. 1-8). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DETC2007-34563

Ehsan, S., Clark, A., Leonardis, A., Rehman, N. U., Khaliq, A., Fasli, M. & McDonald-Maier, K. (2016). A Generic Framework for Assessing the Performance Bounds of Image Feature Detectors. Remote Sensing, 8(11), 928. https://doi.org/10.3390/rs8110928

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