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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Imanberdiyev, N., Sood, S., Kircali, D. & Kayacan, E. (2022). Design, development and experimental validation of a lightweight dual-arm aerial manipulator with a COG balancing mechanism. Mechatronics, 82, Article 102719. https://doi.org/10.1016/j.mechatronics.2021.102719

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

Lillelund, C. M., Jensen, H. B. & Pedersen, C. F. (2022). Cloud K-SVD for Image Denoising. SN Computer Science, 3, Article 151. https://doi.org/10.1007/s42979-022-01042-y

Böttjer, T., Ørnskov Rønsch, G., Gomes, C., Ramanujan, D., Iosifidis, A. & Larsen, P. G. (2021). Data-Driven Identification of Remaining Useful Life for Plastic Injection Moulds. In A.-L. Andersen, R. Andersen, T. D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (Eds.), Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021 (pp. 431-439). Springer. https://doi.org/10.1007/978-3-030-90700-6_49

Gaikwad, N., Liu, L., Griffiths, M. P., Vang, M. Ø., Grombacher, D. & Larsen, J. J. (2022). Thermal Model of the Apsu Transmitter for Lightweight and Compact Heat Sink Design. 11-13. Abstract from The 8th International Workshop on Magnetic Resonance Sounding, Strasbourg, France. https://mrs2021.sciencesconf.org/data/pages/proceedings_MRS2021_distrib_v2.pdf

Asif, M. R., Larsen, J. J., Maurya, P. K., Christiansen, A. V. & Auken, E. (2021). Complete automation of time-domain transient electromagnetic data processing using deep convolutional autoencoder. Abstract from AGU 2021, United States.

Feng, H., Gomes, C., Iosifidis, A. & Larsen, P. G. (2021). Model-based monitoring and state estimation for digital twins: The Kalman filter. In C. R. Martin, M. J. Blas & A. I. Psijas (Eds.), Annual Modeling and Simulation Conference (ANNSIM 2021) (pp. 1-2)

Kjaer-Rasmussen, A., Griffiths, M. P., Grombacher, D. & Larsen, J. J. (2022). Fast removal of powerline harmonic noise from surface NMR datasets using a projection-based approach on graphical processing units. IEEE Geoscience and Remote Sensing Letters, 19, Article 8024005. https://doi.org/10.1109/LGRS.2021.3118064

Larsen, J. J., Griffiths, M., Vang, M., Liu, L. & Grombacher, D. (2021). Apsu - A compact surface NMR instrument for groundwater investigations. SEG Technical Program Expanded Abstracts, 2021-September, 3135-3139. https://doi.org/10.1190/segam2021-3582046.1

Sørensen, R. A., Rosenberg, J. & Karstoft, H. (2021). Baggage Routing with Scheduled Departures using Deep Reinforcement Learning. In Proceedings - 2021 International Symposium on Computer Science and Intelligent Controls, ISCSIC 2021 (pp. 13-19). IEEE. https://doi.org/10.1109/ISCSIC54682.2021.00014

Hedegaard Morsing, L. & Iosifidis, A. (2022). Continual 3D Convolutional Neural Networks for Real-time Processing of Videos. In S. Avidan, G. Brostow, M. Cissé, G. M. Farinella & T. Hassner (Eds.), Computer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings (pp. 369-385). Springer. https://doi.org/10.1007/978-3-031-19772-7_22

Tøttrup, D., Skovgaard, S. L., Sejersen, J. L. F. & de Figueiredo, R. P. (2021). A fast and accurate approach to multiple-vehicle localization and tracking from monocular aerial images. Journal of Imaging, 7(12), Article 270. https://doi.org/10.3390/jimaging7120270

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

Magris, M. & Iosifidis, A. (2021). Approximate Bayes factors for unit root testing. Paper presented at International Association for Applied Econometrics Annual Conference 2021, Rotterdam, Netherlands. https://www.researchgate.net/publication/349492007_Approximate_Bayes_factors_for_unit_root_testing

Böttjer, T., Ørnskov Rønsch, G., Gonçalves Gomes, C. Â., Ramanujan, D., Iosifidis, A. & Larsen, P. G. (2021). Data-Driven Identification of Remaining Useful Life for Plastic Injection Moulds. In A.-L. Andersen, R. Andersen, D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (Eds.), Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems: Proceedings of the Changeable, Agile, Reconﬁgurable and Virtual Production Conference and the World Mass Customization & Personalization Conference (pp. 431-439). Springer. https://www.springerprofessional.de/en/data-driven-identification-of-remaining-useful-life-for-plastic-/19816878

Kass, M. A., Auken, E., Larsen, J. J. & Christiansen, A. V. (2021). A towed magnetic gradiometer array for rapid, detailed imaging of utility, geological, and archaeological targets. Geoscientific Instrumentation, Methods and Data Systems, 10(2), 313-323. https://doi.org/10.5194/gi-10-313-2021

Leporowski, B. T., Tola, D., Hansen, C. & Iosifidis, A. (2022). Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving. In A.-L. Andersen, R. Andersen, T. D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (Eds.), Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021 (pp. 224-232). Springer. https://doi.org/10.1007/978-3-030-90700-6_25

Seker, M., Männistö, A., Iosifidis, A. & Raitoharju, J. (2021). Automatic Main Character Recognition for Photographic Studies. In IEEE 23rd International Workshop on Multimedia Signal Processing, MMSP 2021 IEEE. https://doi.org/10.1109/MMSP53017.2021.9733624

Laakom, F., Raitoharju, J., Nikkanen, J., Iosifidis, A. & Gabbouj, M. (2021). Robust channel-wise illumination estimation. In 32rd British Machine Vision Conference https://arxiv.org/pdf/2111.05681.pdf

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

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2021). Multi-Exit Vision Transformer for Dynamic Inference. In The 32nd British Machine Vision Conference https://www.semanticscholar.org/paper/Multi-Exit-Vision-Transformer-for-Dynamic-Inference-Bakhtiarnia-Zhang/26f61039857423c0aad2c44f0e7769cbb61246c9

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

Laakom, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2021). On Feature Diversity in Energy-based models. Paper presented at International Conference on Learning Representations Workshop on Energy-Based Models, , Vienna. https://openreview.net/pdf?id=ks3Q08yy66r

Chumachenko, K., Laakom, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2021). Rethinking compactness in deep neural networks. Paper presented at International Conference on Machine Learning Workshop on Overparameterization. https://drive.google.com/file/d/1pu5xJMPvKuGFeVCcylEvLz00uLK3UN9I/view

Laakom, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2021). Within-layer Diversity Reduces Generalization Gap. Paper presented at International Conference on Machine Learning Workshop on Information Theoretic Methods for Rigorous, Responsible, and Reliable Machine Learning, 2021. https://arxiv.org/pdf/2106.06012.pdf

Tran, D. T., Kanniainen, J. & Iosifidis, A. (2021). How informative is the Order Book Beyond the Best Levels? Machine Learning Perspective. Paper presented at NeurIPS 2021 Workshop on Machine Learning meets Econometrics. https://doi.org/10.2139/ssrn.3920827

Tran, D. T., Gabbouj, M. & Iosifidis, A. (2022). Remote Multilinear Compressive Learning with Adaptive Compression. IEEE Internet of Things Journal, 9(9), 6905-6913. https://doi.org/10.1109/jiot.2021.3114743

Xu, L., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2022). Saliency-based Weighted Multilabel Linear Discriminant Analysis. IEEE Transactions on Cybernetics, 52(10), 10200-10213. https://doi.org/10.1109/TCYB.2021.3069338

Chumachenko, K., Iosifidis, A. & Gabbouj, M. (2022). Feedforward Neural Networks Initialization based on Discriminant Learning. Neural Networks, 146, 220-229. https://doi.org/10.1016/j.neunet.2021.11.020

Malik, J., Kiranyaz, S., Al-Raoush, R. I., Monga, O., Garnier, P., Foufou, S., Bouras, A., Iosifidis, A., Gabbouj, M. & Baveye, P. C. (2022). 3D Quantum Cuts for Automatic Segmentation of Porous Media in Tomography Images. Computers & Geosciences, 159, Article 105017. https://doi.org/10.1016/j.cageo.2021.105017

Hedegaard Morsing, L., Sheikh-Omar, O. A. & Iosifidis, A. (2021). Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocol. IEEE Transactions on Image Processing, 30, 8619-8631. https://doi.org/10.1109/TIP.2021.3118978

Bajovic, D., Bakhtiarnia, A., Bravos, G., Brutti, A., Burkhardt, F., Cauchi, D., Chazapis, A., Cianco, C., Dall'Asen, N., Delic, V., Dimou, C., Djokic, D., Escobar-Molero, A., Esterle, L., Eyben, F., Farella, E., Festi, T., Geromitsos, A., Giakoumakis, G. ... Zammit, J. (2021). MARVEL: Multimodal Extreme Scale Data Analytics for Smart Cities Environments. In 2021 International Balkan Conference on Communications and Networking, BalkanCom 2021 (pp. 143-147). IEEE. https://doi.org/10.1109/BalkanCom53780.2021.9593258

Bjerge, K., Mann, H. M. R. & Høye, T. T. (2022). Real-time insect tracking and monitoring with computer vision and deep learning. Remote Sensing in Ecology and Conservation, 8(3), 315-327. https://doi.org/10.1002/rse2.245

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

Leporowski, B. & Iosifidis, A. (2021). Visualising deep network time-series representations. Neural Computing and Applications, 33(23), 16489-16498. https://doi.org/10.1007/s00521-021-06244-8

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

Hvilshøj, F., Iosifidis, A. & Assent, I. (2021). ECINN: Efficient Counterfactuals from Invertible Neural Networks. In 32nd British Machine Vision Conference 2021, BMVC 2021, Virtual https://arxiv.org/abs/2103.13701

Bjerge, K., Schougaard, J. H. & Larsen, D. E. (2021). A scalable and efficient convolutional neural network accelerator using HLS for a system-on-chip design. Microprocessors and Microsystems, 87, Article 104363. https://doi.org/10.1016/j.micpro.2021.104363

Kragh, M. F., Rimestad, J., Lassen, J. T., Berntsen, J. & Karstoft, H. (2022). Predicting embryo viability based on self-supervised alignment of time-lapse videos. IEEE Transactions on Medical Imaging, 41(2), 465-475. https://doi.org/10.1109/TMI.2021.3116986

Feng, H., Gomes, C., Thule, C., Lausdahl, K., Iosifidis, A. & Larsen, P. G. (2021). Introduction to Digital Twin Engineering. In C. R. Martin, M. J. Blas & A. I. Psijas (Eds.), 2021 Annual Modeling and Simulation Conference (ANNSIM) (pp. 1-12). IEEE. https://doi.org/10.23919/ANNSIM52504.2021.9552135

Mehndiratta, M., Singh, K., Kayacan, E. & Feroskhan, M. (2021). Receding Horizon-based Fault-tolerant Control of QuadPlus: An Over-actuated Quadrotor. In 2021 IEEE 17th International Conference on Automation Science and Engineering, CASE 2021 (pp. 853-859). IEEE. https://doi.org/10.1109/CASE49439.2021.9551527

Laakom, F., Raitoharju, J., Iosifidis, A., Nikkanen, J. & Gabbouj, M. (2021). Monte Carlo Dropout Ensembles for Robust Illumination Estimation. In IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings IEEE. https://doi.org/10.1109/IJCNN52387.2021.9534314

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2021). Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning. In 2021 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/IJCNN52387.2021.9533875

Heidari, N. & Iosifidis, A. (2021). On the spatial attention in spatio-temporal graph convolutional networks for skeleton-based human action recognition. In IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings IEEE. https://doi.org/10.1109/IJCNN52387.2021.9534440

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

Figueiredo, R. P. D., Hansen, J. G., Le Fevre Sejersen, J., Brandão, M. & Kayacan, E. (2021). On the Advantages of Multiple Stereo Vision Camera Designs for Autonomous Drone Navigation.

le Fevre Sejersen, J., Pimentel De Figueiredo, R. & Kayacan, E. (2021). Safe Vessel Navigation Visually Aided by Autonomous Unmanned Aerial Vehicles in Congested Harbors and Waterways. In 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE) (pp. 1901-1907) https://doi.org/10.1109/CASE49439.2021.9551637

Dyrmann, M., Skovsen, S. K. & Christiansen, P. H. (2021). Camera-based estimation of sugar beet stem points and weed cover using convolutional neural networks. In J. V. Stafford (Ed.), Precision agriculture ’21 (pp. 307-313) https://doi.org/10.3920/978-90-8686-916-9_36

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

Griffiths, M. P., Grombacher, D. J. & Larsen, J. J. (2021). Efficient numerical Bloch solutions for multipulse surface NMR. Geophysical Journal International, 227(3), 1905-1916. https://doi.org/10.1093/gji/ggab321

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