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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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Thanh Tran, D., Kiranyaz, S., Gabbouj, M. & Iosifidis, A. (2020). Heterogeneous Multilayer Generalized Operational Perceptron. IEEE Transactions on Neural Networks and Learning Systems, 31(3), 710-724. Article 8727718. https://doi.org/10.1109/TNNLS.2019.2914082

Laursen, S. R. L., Holm, H. A. & Nielsen, S. H. (2022). Heart rate-based dynamic sound intervention - a pilot study. In Proceedings of the EUROREGIO/BNAM2022 Conference: Joimt Acoustics Conference (pp. 473-481). https://www.conforg.fr/erbnam2022/output_directory/data/articles/000032.pdf

Kayacan, E. & Kaynak, O. (2018). Hata Artıklarını Kullanarak Gri Modellerin Duzeltilmesi: Error Modification of Grey Models using Principle of Concatenation. In SIU2010 - IEEE 18.Sinyal isleme ve iletisim uygulamalari kurultayi - Diyarbakir (pp. 77-80) https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5649067

Malekian, R., Curran, K., Pedersen, C. F., Cao, B. & Qi, X. (2018). Guest Editorial Special Issue on Sensor Technologies for Connected Cars: Devices, Systems and Modeling. IEEE Sensors Journal, 18(12), 4775-4776. https://doi.org/10.1109/JSEN.2018.2830478

Skov, K., Mandrup, L., Johannsen, L. & Lysgaard, P. (2001). Grundlæggende EMC I: Fænomener og konstruktionsprincipper. In Grundlæggende EMC I: Fænomener og konstruktionsproncipper (1.3 ed., Vol. T-306, pp. 164).

Christiansen, M. P., Laursen, M. S., Nyholm Jørgensen, R., Skovsen, S. & Gislum, R. (2018). Ground Vehicle Mapping of Fields using LiDAR to Enable Prediction of Crop Biomass. In Proceedings of the 14th International Conference on Precision Agriculture Article 5145 International Society of Precision Agriculture. https://ispag.org/proceedings/?action=abstract&id=5145

Kayacan, E., Ulutas, B., Buyuksalvarci, A. & Kaynak, O. (2007). Gri Sistem Kurami ve Finansman Uygulamalari:IMKB Ornegi. In UFS 2007 - 11. Ulusal Finans Sempozyumu (pp. 215-229) http://www.academia.edu/2695567/GR%C4%B0_S%C4%B0STEM_KURAMI_VE_F%C4%B0NANSMAN_UYGULAMALARI_%C4%B0MKB_%C3%96RNE%C4%9E%C4%B0

Bozcan, I., Sejersen, J. L. F., Pham, X. H. & Kayacan, E. (2021). GridNet: Image-Agnostic Conditional Anomaly Detection for Indoor Surveillance. IEEE Robotics and Automation Letters, 6(2), 1638 - 1645. Article 9345954. https://doi.org/10.1109/LRA.2021.3057003

Kayacan, E. & Kaynak, O. (2006). Gri Bulanik PID Denetleyici Tasarimi ve Dogrusal Olmayan Sivi Seviye Sistemine Uygulanmasi. In TOK 2006 - Otomatik Kontrol Ulusal Toplantisi (pp. 16-21)

Kayacan, E., Ulutas, B. & Kaynak, O. (2009). Grey System Theory-Based Models in Time Series Prediction. Expert Systems with Applications, 37(2), 1784-1789. https://doi.org/10.1016/j.eswa.2009.07.064

Kayacan, E. & Kaynak, O. (2006). Grey Prediction Based Control of a Non-Linear Liquid Level System Using PID Type Fuzzy Controller. In Mechatronics, 2006 IEEE International Conference on (pp. 292-296). IEEE . https://doi.org/10.1109/ICMECH.2006.252541

Iosifidis, A., Tsapanos, N. & Pitas, I. (2015). Graphs in social and digital media. In I. Pitas (Ed.), Graph-Based Social Media Analysis CRC Press.

Li, N., Iosifidis, A. & Zhang, Q. (2022). Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing. In 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings (pp. 982-987). IEEE. https://doi.org/10.1109/GLOBECOM48099.2022.10001067

Iosifidis, A. & Gabbouj, M. (2016). Graph-regularized Multi-class Support Vector Machines for Face and Action Recognition. In 2016 24th European Signal Processing Conference, EUSIPCO 2016 (pp. 96-100). Article 7760217 IEEE. https://doi.org/10.1109/EUSIPCO.2016.7760217

Laakom, F., Raitoharju, J., Passalis, N., Iosifidis, A. & Gabbouj, M. (2022). Graph Embedding with Data Uncertainty. IEEE Access, 10, 24232-24239. https://doi.org/10.1109/ACCESS.2022.3155233

Sohrab, F., Iosifidis, A., Gabbouj, M. & Raitoharju, J. (2023). Graph-embedded subspace support vector data description. Pattern Recognition, 133, Article 108999. https://doi.org/10.1016/j.patcog.2022.108999

Mygdalis, V., Iosifidis, A., Tefas, A. & Pitas, I. (2016). Graph Embedded One-Class Classifiers for media data classification. Pattern Recognition, 60, 585-595. https://doi.org/10.1016/j.patcog.2016.05.033

Gautam, C., Tiwari, A., Mishra, P. K., Suresh, S., Iosifidis, A. & Tanveer, M. (2021). Graph-Embedded Multi-Layer Kernel Ridge Regression for One-Class Classification. Cognitive Computation, 13(2), 552-569. https://doi.org/10.1007/s12559-020-09804-7

Iosifidis, A., Tefas, A. & Pitas, I. (2015). Graph Embedded Extreme Learning Machine. IEEE Transactions on Cybernetics, 46(1), 311-324. https://doi.org/10.1109/TCYB.2015.2401973

Heidari, N., Hedegaard Morsing, L. & Iosifidis, A. (2022). Graph convolutional networks. In A. Iosifidis & A. Tefas (Eds.), Deep Learning for Robot Perception and Cognition (pp. 71-99). Elsevier. https://doi.org/10.1016/B978-0-32-385787-1.00009-9

Iosifidis, A. & Pitas, I. (2015). Graph-based Pattern Classification and Dimensionality Reduction. In I. Pitas (Ed.), Graph-Based Social Media Analysis CRC Press.

Cao, G., Iosifidis, A., Chen, K. & Gabbouj, M. (2018). Generalized Multi-View Embedding for Visual Recognition and Cross-Modal Retrieval. IEEE Transactions on Cybernetics, 48(9), 2542-2555. Article 8026149. https://doi.org/10.1109/TCYB.2017.2742705

Cao, G., Iosifidis, A., Chen, K. & Gabbouj, M. (2016). Generalized Multi-view Embedding for Visual Recognition and Cross-modal Retrieval. arXiv, 1-14 (+ 3). https://arxiv.org/pdf/1605.09696.pdf

Kiranyaz, S., Ince, T., Iosifidis, A. & Gabbouj, M. (2017). Generalized Model of Biological Neural Networks: Progressive Operational Perceptrons. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings (pp. 2477-2485). Article 7966157 IEEE. https://doi.org/10.1109/IJCNN.2017.7966157

Mehndiratta, M. & Kayacan, E. (2020). Gaussian Process-based Learning Control of Aerial Robots for Precise Visualization of Geological Outcrops. In 2020 European Control Conference (ECC) (pp. 10-16). Article 9143655 IEEE.

Pham, X. H., Bozcan, I., Sarabakha, A., Haddadin, S. & Kayacan, E. (2021). GateNet: An Efficient Deep Neural Network Architecture for Gate Perception Using Fish-Eye Camera in Autonomous Drone Racing. Paper presented at 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, Prag, Czech Republic. https://sarabakha.info/files/papers/conference/IROS_2021.pdf

Camci, E. & Kayacan, E. (2016). Game of Drones: UAV Pursuit-Evasion Game with Type-2 Fuzzy Logic Controllers tuned by Reinforcement Learning. In 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016 (pp. 618-625). Article 7737744 IEEE. https://doi.org/10.1109/FUZZ-IEEE.2016.7737744

Nielsen, M. R., Laursen, M. S., Jonassen, M. S., Nielsen, K. & Nyholm Jørgensen, R. (2013). Gall mite inspection on dormant black currant buds using machine vision. In Precision Agriculture 2013 - Papers Presented at the 9th European Conference on Precision Agriculture, ECPA 2013 (pp. 517-524) http://www.scopus.com/inward/record.url?scp=84893421680&partnerID=8YFLogxK

Kayacan, E. & Ahmadieh Khanesar, M. (2017). Fuzzy Neural Networks for Real Time Control Applications, Concepts, Modeling and Algorithms for Fast Learning. Butterworth-Heinemann. https://books.google.dk/books?id=8X4VBgAAQBAJ&printsec=frontcover&hl=da#v=onepage&q&f=false

Nyholm Jørgensen, R., Sørensen, R. A., Laursen, M. S., Rasmussen, J. & Nielsen, J. (2017). FutureCropping Generering af tidselkort med Pix4D, DeepLearning og QGIS 18AUG2017. Pictures, Video and sound recordings (digital), YouTube. https://www.youtube.com/watch?v=3X8h4U4o2-o

Bertelsen, K. K., Duzel, J., Nielsen, S. H. & Jørgensen, R. N. (2011). FruitGrowth - Gasburning in Orchards - Environment friendly weed control. http://www.horteam-expo.dk/

Kirkelund, G. E. & Bennedsen, J. (2016). From PBL Supervisor to Product Owner: SCRUM based supervision of student projects. In Proceedings of SEFI Annual Conference: SEFI 2016 - the44th annnual conference European Society for Engineering Education (SEFI). http://www.sefi.be/conference-2016/papers/Engineering_Skills/kirkelund-from-pbl-supervisor-to-product-owner-104.pdf

Jensen, K., Bøgild, A., Nielsen, S. H., Christiansen, M. P. & Jørgensen, R. N. (2011). FroboMind, proposing a conceptual architecture for agricultural field robot navigation. http://www.njf.nu/site/seminarRedirect.asp?intSeminarID=441&p=1004

Grombacher, D., Griffiths, M. P., Liu, L., Vang, M. & Larsen, J. J. (2022). Frequency Shifting Steady-State Surface NMR Signals to Avoid Problematic Narrowband-Noise Sources. Geophysical Research Letters, 49(7), Article e2021GL097402. https://doi.org/10.1029/2021GL097402

Sveistrup, D., Jørgensen, R. N., Green, O., Nørremark, M. & Sørensen, C. A. G. (2011). Fremtidens markforsøg. http://pure.agrsci.dk:8080/research/fremtidens_markforsoeg(4162764)/

Bach, P. D., Jørgensen, R. N. & Jensen, K. (2012). Fremtidens landbrugsmaskiner kører selv. Dagbladet Holstebro-Struer, 14-15. https://docs.google.com/open?id=0B7n4HMXh8Cx3Qk1DTHRfMHo2ZEU

Jørgensen, R. (2007). Fremtiden for Forskningscenter Bygholm. Horsens Folkeblad, 2.

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. (2020). FPGA-Based Design for Online Computation of Multivariate Empirical Mode Decomposition. IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 67(12), 5040-5050. Article 9159690. https://doi.org/10.1109/TCSI.2020.3012351

Jensen, T., Green, O., Munkholm, L. J. & Karstoft, H. (2016). Fourier and granulometry methods on 3D images of soil surfaces for evaluating soil aggregate size distribution. American Society of Agricultural and Biological Engineers. Transactions, 32(5), 609-615. https://doi.org/10.13031/aea.32.10938

Griffiths, M. P., Grombacher, D., Liu, L., Vang, M. Ø. & Larsen, J. J. (2022). Forward Modeling Steady-State Free Precession in Surface NMR. IEEE Geoscience and Remote Sensing Letters, 60, Article 4513510. https://doi.org/10.1109/TGRS.2022.3221624

Tu, G. J., Karstoft, H., Pedersen, L. J. & Jørgensen, E. (2013). Foreground detection using loopy belief propagation. Biosystems Engineering, 116(1), 88-96. https://doi.org/10.1016/j.biosystemseng.2013.06.011

Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2017). Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks. In Proceedings - 2017 IEEE 19th Conference on Business Informatics, CBI 2017 (pp. 7-12). Article 8010701 IEEE. https://doi.org/10.1109/CBI.2017.23

Mäkinen, M., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2019). Forecasting of Jump Arrivals in Stock Prices: New Attention-based Network Architecture using Limit Order Book Data. arxiv.org. https://arxiv.org/pdf/1810.10845.pdf

Nielsen, A. H., Iosifidis, A. & Karstoft, H. (2022). Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data. Scientific Reports, 12(1), Article 8395. https://doi.org/10.1038/s41598-022-12167-8

Makinen, Y., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2019). Forecasting jump arrivals in stock prices: new attention-based network architecture using limit order book data. Quantitative Finance, 19(12), 2033-2050 . https://doi.org/10.1080/14697688.2019.1634277

Passalis, N., Kanniainen, J., Gabbouj, M., Iosifidis, A. & Tefas, A. (2021). Forecasting Financial Time Series Using Robust Deep Adaptive Input Normalization. Journal of Signal Processing Systems, 93(10), 1235-1251. https://doi.org/10.1007/s11265-020-01624-0

Wilson Lee, Y. J., Mehndiratta, M. & Kayacan, E. (2018). Fly without borders with Additive Manufacturing: A microscale tilt-rotor Tricopter Design. In Proceedings of the 3rd International Conference on Progress in Additive Manufacturing (Pro-AM 2018) (Vol. 2018-May, pp. 256-261) https://doi.org/10.25341/D43K5G

Jørgensen, R. (2007). Flyt ikke studiet væk fra de studerende. Horsens Folkeblad, 2, 2. sektion.

Olesen, C. R., Nyholm Jørgensen, R., Nielsen, L. & Fris, H. J. (2015). Flere og flere rådyr bliver ofre ved græsslæt. Effektivt Landbrug.

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