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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Chumachenko, K., Mannisto, A., Iosifidis, A. & Raitoharju, J. (2020). Machine Learning Based Analysis of Finnish World War II Photographers. IEEE Access, 8, 144184-144196. Article 9159591. https://doi.org/10.1109/ACCESS.2020.3014458

Islam, M. T., Khan, H. A., Naveed, K., Nauman, A., Gulfam, S. M. & Kim, S. W. (2023). LUVS-Net: A Lightweight U-Net Vessel Segmentor for Retinal Vasculature Detection in Fundus Images. Electronics, 12(8), Article 1786. https://doi.org/10.3390/electronics12081786

Shabani, M. & Iosifidis, A. (2020). Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting. In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 2156-2161). Article 9308440 IEEE. https://doi.org/10.1109/SSCI47803.2020.9308440

Liu, L., Grombacher, D. J., Auken, E. & Larsen, J. J. (2018). Low noise, multichannel surface NMR receiver system with wireless connections to receiver coils. Abstract from First Australian Exploration Geoscience Conference, Sydney, Australia. http://www.publish.csiro.au/ex/pdf/ASEG2018abT7_1H

Alvarez Tunon, O., Brodskiy, Y. & Kayacan, E. (2023). Loss it right: Euclidean and riemannian metrics in learning-based visual odometry. In ISR Europe 2023: 56th International Symposium on Robotics, in cooperation with Fraunhofer IPA September 26 – 27, 2023 in Stuttgart (pp. 107-111). VDE Verlag GmbH. https://ieeexplore.ieee.org/abstract/document/10363060

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.

Tu, G. J. & Karstoft, H. (2015). Logarithmic dyadic wavelet transform with its applications in edge detection and reconstruction. Applied Soft Computing, 26, 193-201. https://doi.org/10.1016/j.asoc.2014.09.044

Christiansen, M. P., Jensen, K., Ellekilde, L.-P. & Jørgensen, R. N. (2011). Localization in orchards using Extended Kalman Filter for sensor-fusion - A FroboMind component.. http://www.njf.nu/site/seminarRedirect.asp?intSeminarID=441&p=1004

Jørgensen, R. & Rasmussen, P. (2002). Line Imaging Spectrometer Sampler (LISS) - a user manual.

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

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

Ahmadieh Khanesar, M., Kayacan, E., Teshnelab, M. & Kaynak, O. (2011). Levenberg Marquardt Algorithm for the Training of Type-2 Fuzzy Neuro Systems with a Novel Type-2 Fuzzy Membership Function. In Advances in Type-2 Fuzzy Logic Systems (T2FUZZ), 2011 IEEE Symposium on (pp. 88-93). IEEE. https://doi.org/10.1109/T2FUZZ.2011.5949558

Ahmadieh Khanesar, M. & Kayacan, E. (2015). Levenberge-Marquardt Training Method for Type-2 Fuzzy Neural Networks and Its Stability Analysis. In Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on (pp. 1-7). IEEE. https://doi.org/10.1109/FUZZ-IEEE.2015.7338101

Kusk, K., Nielsen, D. B., Thylstrup, T., Rasmussen, N. H., Jørvang, J., Pedersen, C. F. & Wagner, S. R. (2012). Lessons Learned From a Lightweight Context-Aware System for Achieving Reliable Home Blood Pressure Self-Measurements. Paper presented at NordiCHI 2012, The Nordic Conference on Human-Computer Interaction, Copenhagen, Denmark.

Tzogka, C., Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2019). Less is more: Deep Learning using subjective annotations for sentiment analysis from social media. In 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019: Proceedings Article 8918792 IEEE. https://doi.org/10.1109/MLSP.2019.8918792

Pedersen, C. F. (2011). Leja ordering LSFs for accurate estimation of predictor coefficients. Proceedings of the International Conference on Spoken Language Processing.

Jørgensen, R. N. (2011). Leg med store perspektiver. Land&Liv, 2011(uge 25), 13. http://www.e-pages.dk/landogliv/5/13

Tran, D. T. & Iosifidis, A. (2019). Learning to Rank: A Progressive Neural Network Learning Approach. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 8355-8359). Article 8683711 IEEE. https://doi.org/10.1109/ICASSP.2019.8683711

Laakom, F., Chumachenko, K., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2021). Learning to ignore: rethinking attention in CNNs. In 32rd British Machine Vision Conference https://arxiv.org/pdf/2111.05684.pdf

Iosifidis, A., Tefas, A. & Pitas, I. (2013). Learning sparse representations for view-independent human action recognition based on fuzzy distances. Neurocomputing, 121, 344-353. https://doi.org/10.1016/j.neucom.2013.05.021

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

Kayacan, E., Kayacan, E., Ramon, H. & Saeys, W. (2014). Learning in Centralized Nonlinear Model Predictive Control: Application to an Autonomous Tractor-Trailer System. IEEE Transactions on Control Systems Technology, 23(1), 197-205. https://doi.org/10.1109/TCST.2014.2321514

Iosifidis, A., Tefas, A., Nikolaidis, N. & Pitas, I. (2011). Learning Human Identity Using View-Invariant Multi-view Movement Representation. In C. Vielhauer, J. Dittmann, A. Drygajlo, N. C. Juul & M. C. Fairhurst (Eds.), Biometrics and ID Management: COST 2101 European Workshop, BioID 2011, Brandenburg (Havel), Germany, March 8-10, 2011. Proceedings (pp. 217-226). Springer. https://doi.org/10.1007/978-3-642-19530-3_20

Aytekin, C., Iosifidis, A., Kiranyaz, S. & Gabbouj, M. (2016). Learning Graph Affinities for Spectral Graph-based Salient Object Detection. Pattern Recognition, 64, 159-167. https://doi.org/10.1016/j.patcog.2016.11.005

Laakom, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2023). Learning Distinct Features Helps, Provably. In D. Koutra, C. Plant, M. Gomez Rodriguez, E. Baralis & F. Bonchi (Eds.), Machine Learning and Knowledge Discovery in Databases: Research Track: European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part II (pp. 206-222). Springer. https://doi.org/10.1007/978-3-031-43415-0_13

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

Kayacan, E., Ahmadieh Khanesar, M., Rubio-Hervas, J. & Reyhanoglu, M. (2017). Learning control of fixed-wing unmanned aerial vehicles using fuzzy neural networks. International Journal of Aerospace Engineering, 2017, Article 5402809. https://doi.org/doi.org/10.1155/2017/5402809

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

Riabchenko, E., Meissner, K., Ahmad, I., Iosifidis, A., Tirronen, V., Gabbouj, M. & Kiranyaz, S. (2016). Learned vs. Engineered Features for Fine-Grained Classification of Aquatic Macroinvertebrates. In 2016 23rd International Conference on Pattern Recognition, ICPR 2016 (pp. 2276-2281). Article 7899975 IEEE. https://doi.org/10.1109/ICPR.2016.7899975

Oleksiienko, I. & Iosifidis, A. (2023). Layer Ensembles. In 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP) IEEE. https://doi.org/10.1109/MLSP55844.2023.10286005

Beevi, F. H. A., Pedersen, C. F., Wagner, S. R. & Hallerstede, S. (2014). Lateral Fall Detection via Events in Linear Prediction Residual of Acceleration. In C. Ramos, P. Novais, C. Ehrwein Nihan & J. M. Corchado Rodriguez (Eds.), Ambient Intelligence - Software and Applications: 5th International Symposium on Ambient Intelligence (Vol. 291, pp. 201-208). Springer. https://doi.org/10.1007/978-3-319-07596-9_22

Larsen, J. J. (2000). Laser induced alignment of neutral molecules. Institut for Fysik og Astronomi, Aarhus Universitet. http://phys.au.dk/fileadmin/site_files/publikationer/phd/Jakob_Juul_Larsen.pdf

Iosifidis, A., Tefas, A. & Pitas, I. (2015). Large-scale nonlinear facial image classification based on Approximate Kernel Extreme Learning Machine. In Image Processing (ICIP), 2015 IEEE International Conference on (pp. 2449-2453). IEEE. https://doi.org/10.1109/ICIP.2015.7351242

Mygdalis, V., Iosifidis, A., Tefas, A. & Pitas, I. (2015). Large-scale classification by an Approximate Least Squares One-Class Support Vector Machine ensemble. In Trustcom/BigDataSE/ISPA, 2015 IEEE (pp. 6-10). IEEE. https://doi.org/10.1109/Trustcom.2015.555

Mygdalis, V., Iosifidis, A., Tefas, A. & Pitas, I. (2016). Laplacian One Class Extreme Learning Machine for Human Action Recognition. In 2016 IEEE 18th International Workshop on Multimedia Signal Processing, MMSP 2016 Article 7813387 IEEE. https://doi.org/10.1109/MMSP.2016.7813387

Rathish, H., Picón, G. C. & Schulz, H.-J. (2024). LaNe Plot: A Visual Fingerprinting Technique for Sequential Data. In K. Kucher, A. Diehl & C. Gillmann (Eds.), Poster Proceedings of the 26th Eurographics Conference on Visualization The Eurographics Association. https://doi.org/10.2312/evp.20241087

Pedersen, C. F., Trier Lund, M., Vardinghus Nielsen, C., Fromberg, J., Sloth, S., Bay Velling, M., Staach, L. & Lorenzen, T. (2000). Læring med IT: Novo Nordisk Projektet. Pictures, Video and sound recordings (digital), Novo Nordisk and The Danish Ministry of Education.

Karstoft, H. (2010). Kursus i Wavelets for ingeniører: Noter til Tisib2. Ingeniørhøjskolen i Aarhus.

Jørgensen, R. & Hansen, P. M. (2000). Kunstigt øje vogter over planters ernæringstilstand. Risoenyt, 2, 12-13.

Green, O. & Jørgensen, R. (2007). Kørselsskader i græs. Poster session presented at Temadag på Institut for Jordbrugsteknik - et vindue til fremtidens markteknologi, Horsens.

Thanh Tran, D., Kiranyaz, S., Gabbouj, M. & Iosifidis, A. (2019). Knowledge Transfer For Face Verification Using Heterogeneous Generalized Operational Perceptrons. In 2019 IEEE International Conference on Image Processing (ICIP) (pp. 1168-1172). Article 8804296 IEEE. https://doi.org/10.1109/ICIP.2019.8804296

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

Kayacan, E., Oniz, Y., Kaynak, O. & V. Topalev, A. (2008). Kilitlenmeyen Fren Sistemi (KFS)’nin Gri cok Katmanli Ileri Beslemeli Yapay Sinir Aglari ile Uyarlamali Kontrolu. In TOK 2008 Otomatik Kontrol Ulusal Toplantisi (pp. 453-458)

Oniz, Y., Kayacan, E. & Kaynak, O. (2007). Kilitlenmeyen Fren Sistemi (KFS) icin Gri Kayma Kipli Denetleyici Tasarimi. In TOK 2007 - Otomatik Kontrol Ulusal Toplantisi (pp. 506-511)

Mygdalis, V., Iosifidis, A., Tefas, A. & Pitas, I. (2016). Kernel Subclass Support Vector Description for Face and Human Action Recognition. In 2016 1st International Workshop on Sensing, Processing and Learning for Intelligent Machines, SPLINE 2016 - Proceedings Article 7528409 IEEE. https://doi.org/10.1109/SPLIM.2016.7528409

Iosifidis, A., Tefas, A. & Pitas, I. (2014). Kernel Reference Discriminant Analysis. Pattern Recognition Letters, 49, 85-91. https://doi.org/10.1016/j.patrec.2014.06.013

Høye, T. T., Mann, H. M. R. & Bjerge, K. (2020). Kamerabaseret overvågning af Insekter på grønne bytage. Aarhus University, DCE - Danish Centre for Environment and Energy. Videnskabelig rapport fra DCE - Nationalt Center for Miljø og Energi No. 371 https://dce2.au.dk/pub/SR371.pdf

Nielsen, A. H., Iosifidis, A. & Karstoft, H. (2021). IrradianceNet: Spatiotemporal Deep Learning Model for Satellite-Derived Solar Irradiance Nowcasting. Solar Energy, 228, 659-669. https://doi.org/10.1016/j.solener.2021.09.073

Amarloo, A., Cinnella, P., Iosifidis, A., Forooghi, P. & Abkar, M. (2023). Investigation of Data-driven algebraic Reynolds stress models for turbulent secondary flows. In The 14th International ERCOFTAC Symposium on Engineering Turbulence Modelling and Measurements Proceedings https://www.ercoftac.org/etmm/program/conference-program/

Asif, M. R., Maurya, P. K., Larsen, J. J. & Christiansen, A. V. (2022). Investigating the Integration of Neural Networks in Least-Squares Method for Airborne Electromagnetic Data Inversion. 1-5. Paper presented at NSG2022 3rd Conference on Airborne, Drone and Robotic Geophysics, Beograd, Serbia. https://doi.org/10.3997/2214-4609.202220034

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Revised 10.08.2023

AU Engineering