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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McLachlan, P. J., Khare, S. K., Grombacher, D., Larsen, J. J., Christensen, A. & Zamora Luria, J. C. (2022). On The Presence of Correlated Noise in Transient Electromagnetic (Tem) Monitoring Data. I NSG2022 28th European Meeting of Environmental and Engineering Geophysics (s. 1-5). European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.202220119

Dobbs, A. M., Ginn, D., Skovsen, S. K., Bagavathiannan, M. V., Mirsky, S. B., Reberg-Horton, C. S. & Leon, R. G. (2022). New directions in weed management and research using 3D imaging. Weed Science, 70(6), 641-647. https://doi.org/10.1017/wsc.2022.56

Dong, Z., Li, N., Iosifidis, A. & Zhang, Q. (2022). Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing. ArXiv. https://doi.org/10.48550/arXiv.2211.13778

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

McLachlan, P. J., Khare, S. K., Grombacher, D., Larsen, J. J., A. Christiensen, A. & Luria, J. C. Z. (2022). On The Presence of Correlated Noise in Transient Electromagnetic (Tem) Monitoring Data. 1-5. Abstract fra NSG2022 28th European Meeting of Environmental and Engineering Geophysics, Belgrade , Serbien. https://doi.org/10.3997/2214-4609.202220119

Asif, M. R., Larsen, J. J., Auken, E. & Christiansen, A. V. (2022). Deep Convolutional Auto-Encoder for Automated Processing of Airborne Time-Domain Electromagnetic Data. 1-5. Afhandling præsenteret på NSG2022 3rd Conference on Airborne, Drone and Robotic Geophysics, Beograd, Serbien. https://doi.org/10.3997/2214-4609.202220038

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. Afhandling præsenteret på NSG2022 3rd Conference on Airborne, Drone and Robotic Geophysics, Beograd, Serbien. https://doi.org/10.3997/2214-4609.202220034

Asif, M. R., Foged, N., Bording, T. S., Larsen, J. J. & Christiansen, A. V. (2023). DL-RMD: a geophysically constrained electromagnetic resistivity model database for deep learning applications. Earth System Science Data, 15(3), 1389-1401. https://doi.org/10.5194/essd-2022-345, https://doi.org/10.5194/essd-15-1389-2023

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), Artikel 109. https://doi.org/10.3390/robotics11050109

Pham, X. H., Sarabakha, A., Odnoshyvkin, M. & Kayacan, E. (2022). PencilNet: Zero-Shot Sim-to-Real Transfer Learning for Robust Gate Perception in Autonomous Drone Racing. IEEE Robotics and Automation Letters, 7(4), 11847 - 11854. https://doi.org/10.1109/LRA.2022.3207545

Larsen, J. J., Lévy, L. & Asif, M. R. (2022). Removal of powerline noise in geophysical data sets with a scientific machine-learning based approach. IEEE Transactions on Geoscience and Remote Sensing, 60, Artikel 5923410. https://doi.org/10.1109/TGRS.2022.3223737

Kass, M. A., Grombacher, D., Griffiths, M., Vang, M. Ø., Liu, L. & Larsen, J. J. (2022). A steady-state approach to surface nuclear magnetic resonance. I Proceedings of the Symposium on the Application of Geophyics to Engineering and Environmental Problems, SAGEEP (s. 54). J and N Group, Ltd.. https://www.eegs.org/assets/docs/Symposium/2022SAGEEP/AbstractsbyAuthor/Kass%2C%20A%20STEADY-STATE%20APPROACH%20TO%20SURFACE%20NUCLEAR%20MAGNETIC%20RESONANCE%20-%20SAGEEP%202022%20-%20205367449.pdf

Pimentel de Figueiredo, R., Le Fevre Sejersen, J., Grimm Hansen, J. & Brandão, M. (2022). Integrated design-sense-plan architecture for autonomous geometric-semantic mapping with UAVs. Frontiers in Robotics and AI, 9, Artikel 911974. https://doi.org/10.3389/frobt.2022.911974

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

Bakhtiarnia, A., Leporowski, B., Esterle, L. & Iosifidis, A. (2022). Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications. I 2022 IEEE International Smart Cities Conference (ISC2) IEEE. https://doi.org/10.1109/ISC255366.2022.9921939

Andersen, K. F., Pham, H. X., Ugurlu, H. I. & Kayacan, E. (2022). Event-based Navigation for Autonomous Drone Racing with Sparse Gated Recurrent Network. I 2022 European Control Conference (ECC) (s. 1342-1348). IEEE. https://doi.org/10.23919/ECC55457.2022.9838538

Besson, M., Alison, J., Bjerge, K., Gorochowski, T. E., Høye, T. T., Jucker, T., Mann, H. M. R. & Clements, C. F. (2022). Towards the fully automated monitoring of ecological communities. Ecology Letters, 25(12), 2753-2775. https://doi.org/10.1111/ele.14123

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, Artikel 4513510. https://doi.org/10.1109/TGRS.2022.3221624

Morsing, L. H., Heidari, N. & Iosifidis, A. (2022). Human Activity Recognition. I A. Iosifidis & A. Tefas (red.), Deep Learning for Robot Perception and Cognition (s. 341-370). Elsevier. https://doi.org/10.1016/B978-0-32-385787-1.00019-1

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

Hedegaard Morsing, L., Alok, A., Jose, J. & Iosifidis, A. (2022). Structured Pruning Adapters. https://doi.org/10.48550/arXiv.2211.10155

Hedegaard Morsing, L., Bakhtiarnia, A. & Iosifidis, A. (2022). Continual Transformers: Redundancy-Free Attention for Online Inference. The Eleventh International Conference on Learning Representations. https://arxiv.org/abs/2201.06268

Hedegaard Morsing, L., Heidari, N. & Iosifidis, A. (2023). Continual Spatio-Temporal Graph Convolutional Networks. Pattern Recognition, 140(C), Artikel 109528. https://doi.org/10.1016/j.patcog.2023.109528

Shabani, M., Magris, M., Tzagkarakis, G., Kanniainen, J. & Iosifidis, A. (2023). Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots. Neural Computing and Applications, 35(25), 18519–18531. https://doi.org/10.1007/s00521-023-08674-y

Shabani, M., Tran, D. T., Magris, M., Kanniainen, J. & Iosifidis, A. (2022). Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction. I 30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings (s. 1487-1491). IEEE. https://doi.org/10.23919/EUSIPCO55093.2022.9909957

Iqbal, S., Khan, T. M., Naveed, K., Naqvi, S. S. & Nawaz, S. J. (2022). Recent trends and advances in fundus image analysis: A review. Computers in Biology and Medicine, 151, Artikel 106277. https://doi.org/10.1016/j.compbiomed.2022.106277

Oleksiienko, I., Tran, D. T. & Iosifidis, A. (2022). Variational Neural Networks implementation in Pytorch and JAX[Formula presented]. Software Impacts, 14, Artikel 100431. https://doi.org/10.1016/j.simpa.2022.100431

Asif, M. R., Maurya, P. K., Foged, N., Larsen, J. J., Auken, E. & Christiansen, A. V. (2022). Automated transient electromagnetic data processing for ground-based and airborne systems by a deep learning expert system. IEEE Transactions on Geoscience and Remote Sensing, 60, Artikel 5919814. https://doi.org/10.1109/TGRS.2022.3202304

Høye, T. T., Dyrmann, M., Kjær, C., Nielsen, J., Bruus, M., Mielec, C. L., Vesterdal, M. S., Bjerge, K., Madsen, S. A., Jeppesen, M. R. & Melvad, C. (2022). Accurate image-based identification of macroinvertebrate specimens using deep learning — How much training data is needed? PeerJ, 10, Artikel e13837. https://doi.org/10.7717/peerj.13837

Tran, D. T., Passalis, N., Tefas, A., Gabbouj, M. & Iosifidis, A. (2022). Attention-Based Neural Bag-of-Features Learning for Sequence Data. IEEE Access, 10, 45542-45552. https://doi.org/10.1109/ACCESS.2022.3169776

Li, N., Iosifidis, A. & Zhang, Q. (2022). Distributed Deep Learning Inference Acceleration using Seamless Collaboration in Edge Computing. I ICC 2022 - IEEE International Conference on Communications (s. 3667-3672). IEEE. https://doi.org/10.1109/ICC45855.2022.9839083

Li, N., Iosifidis, A. & Zhang, Q. (2022). Receptive Field-based Segmentation for Distributed CNN Inference Acceleration in Collaborative Edge Computing. I ICC 2022 - IEEE International Conference on Communications (s. 4281-4286). IEEE. https://doi.org/10.1109/ICC45855.2022.9838547

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, Artikel e00331. https://doi.org/10.1016/j.ohx.2022.e00331

Pham, H. X., Ugurlu, H. I., Fevre, J. L., Bardakci, D. & Kayacan, E. (2022). Deep learning for vision-based navigation in autonomous drone racing. I A. Iosifidis & A. Tefas (red.), Deep Learning for Robot Perception and Cognition (s. 371-406). Elsevier. https://doi.org/10.1016/B978-0-32-385787-1.00020-8

Kremer, T., Irons, T., Müller-Petke, M. & Juul Larsen, J. (2022). Review of Acquisition and Signal Processing Methods for Electromagnetic Noise Reduction and Retrieval of Surface Nuclear Magnetic Resonance Parameters. Surveys in Geophysics, 43(4), 999-1053. https://doi.org/10.1007/s10712-022-09695-3

Li, N., Iosifidis, A. & Zhang, Q. (2022). Collaborative edge computing for distributed CNN inference acceleration using receptive field-based segmentation. Computer Networks, 214, Artikel 109150. https://doi.org/10.1016/j.comnet.2022.109150

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

Wan, S., Chen, C. & Iosifidis, A. (2022). Editorial to special issue on cross-media learning for visual question answering. Image and Vision Computing, 118, Artikel 104355. https://doi.org/10.1016/j.imavis.2021.104355

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

Asif, M. R., Foged, N., Maurya, P. K., Grombacher, D. J., Christiansen, A. V., Auken, E. & Larsen, J. J. (2022). Integrating neural networks in least-squares inversion of airborne time-domain electromagnetic data. Geophysics, 87(4), E177-E187. https://doi.org/10.1190/geo2021-0335.1

Tøttrup, D., Skovgaard, S. L., Sejersen, J. L. F. & de Figueiredo, R. P. (2022). A Real-Time Method for Time-to-Collision Estimation from Aerial Images. Journal of Imaging, 8(3), Artikel 62. https://doi.org/10.3390/jimaging8030062

Rehman, N. U., Park, C., Huang, N. E. & Mandic, D. P. (2013). Emd via memd: Multivariate noise-aided computation of standard emd. Advances in Adaptive Data Analysis, 05(02), 1350007. https://doi.org/10.1142/s1793536913500076

Rehman, N., Khan, M. M., Sohaib, M. I., Jehanzaib, M., Ehsan, S. & McDonald-Maier, K. (2014). Image fusion using multivariate and multidimensional EMD. I 2014 IEEE International Conference on Image Processing (ICIP) IEEE. https://doi.org/10.1109/icip.2014.7026035

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

Sadiq, M. T., Yu, X., Yuan, Z., Aziz, M. Z., Rehman, N. U., Ding, W. & Xiao, G. (2022). Motor Imagery BCI Classification Based on Multivariate Variational Mode Decomposition. IEEE Transactions on Emerging Topics in Computational Intelligence, 6(5), 1177-1189. https://doi.org/10.1109/tetci.2022.3147030

Asif, M. R., Maurya, P. K., Christiansen, A. V., Larsen, J. J. & Auken, E. (2022). Deep learning based expert system to automate time-domain electromagnetic data processing. I 34th Symposium on the Application of Geophysics to Engineering and Environmental Problems, SAGEEP 2022 (s. 6). J and N Group, Ltd..

Liu, Z., Liang, J., Zhang, S. & Kayacan, E. (2022). ANN-Based vibration control of an aerial refueling hose system with input nonlinearity and prescribed output constraint. Journal of the Franklin Institute, 359(6), 2627-2645. https://doi.org/10.1016/j.jfranklin.2022.01.006

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), Artikel e2021GL097402. https://doi.org/10.1029/2021GL097402

Khan, T. M., Khan, M. A. U., Rehman, N. U., Naveed, K., Afridi, I. U., Naqvi, S. S. & Raazak, I. (2022). Width-wise vessel bifurcation for improved retinal vessel segmentation. Biomedical Signal Processing and Control, 71(Part A), Artikel 103169. https://doi.org/10.1016/j.bspc.2021.103169

Larsen, J. J., Langhof, R. B., Kjær, M. W., Vang, M., Liu, L., Griffiths, M. & Grombacher, D. (2022). Efficient processing of surface NMR data with spectral analysis. Geophysical Journal International, 229(1), 286-298. https://doi.org/10.1093/gji/ggab472

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Revideret 10.08.2023

AU Engineering