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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Bjerge, K., Alison, J., Dyrmann, M., Frigaard, C. E., Mann, H. M. R. & Høye, T. T. (2023). Accurate detection and identification of insects from camera trap images with deep learning. PLOS Sustainability and Transformation, 2(3). https://doi.org/10.1371/journal.pstr.0000051

Böttjer, T., Tola, D., Kakavandi, F., Wewer, C. R., Ramanujan, D., Gomes, C., Larsen, P. G. & Iosifidis, A. (2023). A review of unit level digital twin applications in the manufacturing industry. CIRP Journal of Manufacturing Science and Technology, 45, 162-189. https://doi.org/10.1016/j.cirpj.2023.06.011

Griffiths, M. P., Grombacher, D., Kass, M. A., Vang, M., Liu, L. & Larsen, J. J. (2023). A surface NMR forward in a dot product. Geophysical Journal International, 234(3), 2284-2290. https://doi.org/10.1093/gji/ggad203

Jensen, R. I. T., Ferwerda, J., Jørgensen, K. S., Jensen, E. R., Borg, M., Krogh, M. P., Jensen, J. B. & Iosifidis, A. (2023). A synthetic data set to benchmark anti-money laundering methods. Scientific Data, 10(1), Article 661. https://doi.org/10.1038/s41597-023-02569-2

Shabani, M., Tran, D. T., Kanniainen, J. & Iosifidis, A. (2023). Augmented bilinear network for incremental multi-stock time-series classification. Pattern Recognition, 141, Article 109604. https://doi.org/10.1016/j.patcog.2023.109604

Magris, M., Shabani, M. & Iosifidis, A. (2023). Bayesian bilinear neural network for predicting the mid-price dynamics in limit-order book markets. Journal of Forecasting, 42(6), 1407-1428. https://doi.org/10.1002/for.2955

Magris, M. & Iosifidis, A. (2023). Bayesian learning for neural networks: an algorithmic survey. Artificial Intelligence Review, 56(10), 11773-11823. https://doi.org/10.1007/s10462-023-10443-1

Gislum, R., Larsen, R., Thomsen, I. K., Greve, M. B., Hansen, E. M. & Nyholm Jørgensen, R., (2023). Betingelse for indsamling af data til brug for omregningsfaktor mellem NDV/NDRE og planteoptag af kvælstof, No. 2023-0543874, 8 p., Aug 23, 2023. Rådgivningsnotat fra DCA - Nationalt Center for Fødevarer og Jordbrug

Agerskov, J., Nielsen, K. & Pedersen, C. F. (2023). Computationally Efficient Labeling of Cancer-Related Forum Posts by Non-Clinical Text Information Retrieval. SN Computer Science, 4(6), Article 711. https://doi.org/10.1007/s42979-023-02244-8

Legaard, C. M., Schranz, T., Schweiger, G., Drgona, J., Falay, B., Gomes, C., Iosifidis, A., Abkar, M. & Larsen, P. G. (2023). Constructing Neural Network Based Models for Simulating Dynamical Systems. ACM Computing Surveys, 55(11), 1-34. Article 236. https://doi.org/10.1145/3567591

Hedegaard, L. & Iosifidis, A. (2023). Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch. In L. Karlinsky, T. Michaeli & K. Nishino (Eds.), Computer Vision – ECCV 2022 Workshops, Proceedings (pp. 21-34). Springer. https://doi.org/10.1007/978-3-031-25082-8_2

Eriksen, T. & Rehman, N. U. (2023). Data-driven nonstationary signal decomposition approaches: a comparative analysis. Scientific Reports, 13(1), Article 1798. https://doi.org/10.1038/s41598-023-28390-w

Amarloo, A., Cinnella, P., Iosifidis, A., Forooghi, P. & Abkar, M. (2023). Data-driven Reynolds stress models based on the frozen treatment of Reynolds stress tensor and Reynolds force vector. Physics of Fluids, 35(7), Article 075154. https://doi.org/10.1063/5.0160977

Jakobsen, M. O., Herskind, E. S., Pedersen, C. F. & Knudsen, M. B. (2023). Detecting insufficient lubrication in rolling bearings, using a low cost MEMS microphone to measure vibrations. Mechanical Systems and Signal Processing, 200, Article 110553. https://doi.org/10.1016/j.ymssp.2023.110553

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

Pham, X. H. (2023). Efficient deep learning-based monocular 3D perception for agile robot navigation. Aarhus Universitet.

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

Bjerge, K., Geissmann, Q., Alison, J., Mann, H. M. R., Høye, T. T., Dyrmann, M. & Karstoft, H. (2023). Hierarchical classification of insects with multitask learning and anomaly detection. Ecological Informatics, 77, Article 102278. https://doi.org/10.1016/j.ecoinf.2023.102278

Gheisarnejad Chirani, M., Mosayebi, M., Rafiei Foroushani, M., Boudjadar, J. & Khooban, M. H. (2023). Hybrid Power Systems for Smart Marine Power Grids: Demonstration and Case Study. In Electric Transportation Systems in Smart Power Grids (pp. 473-492). CRC Press. https://doi.org/10.1201/9781003293989

Haldrup, M., Rasmussen, M., Mohamad, N., Dyrskog, S., Thorup, L., Mikic, N., Wismann, J., Grønhøj, M., Poulsen, F. R., Nazari, M., Rehman, N. U., Simonsen, C. Z. & Korshøj, A. R. (2023). Intraventricular Lavage vs External Ventricular Drainage for Intraventricular Hemorrhage: A Randomized Clinical Trial. JAMA network open, 6(10), e2335247. Article e2335247. https://doi.org/10.1001/jamanetworkopen.2023.35247

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

Wang, Z., Chen, L., Chen, H. & Rehman, N. U. (2023). Monthly ship price forecasting based on multivariate variational mode decomposition. Engineering Applications of Artificial Intelligence, 125, Article 106698. https://doi.org/10.1016/j.engappai.2023.106698

Bjerge, K., Frigaard, C. E. & Karstoft, H. (2023). Object Detection of Small Insects in Time-Lapse Camera Recordings. Sensors, 23(16), Article 7242. https://doi.org/10.3390/s23167242

Hansen, E. M., Thomsen, I. K., Cronin, I. V., Gislum, R., Larsen, R. & Mortensen, A. K., (2023). Opdatere trappemodel om dækningsgrad af efterafgrøder ved kontrol i forhold til etableringstidspunkt, No. 2022-0445946, 18 p., Apr 26, 2023. Rådgivningsnotat fra DCA - Nationalt Center for Fødevarer og Jordbrug

Zehtabiyan-Rezaie, N., Iosifidis, A. & Abkar, M. (2023). Physics-Guided Machine Learning for Wind-Farm Power Prediction: Toward Interpretability and Generalizability. PRX Energy, 2(1), Article 013009. https://doi.org/10.1103/PRXEnergy.2.013009

Baltakys, K., Baltakienė, M., Heidari, N., Iosifidis, A. & Kanniainen, J. (2023). Predicting the trading behavior of socially connected investors: Graph neural network approach with implications to market surveillance. Expert Systems with Applications, 228, Article 120285. https://doi.org/10.1016/j.eswa.2023.120285

Jensen, R. I. T. & Iosifidis, A. (2023). Qualifying and raising anti-money laundering alarms with deep learning. Expert Systems with Applications, 214, Article 119037. https://doi.org/10.1016/j.eswa.2022.119037

Iqbal, S., Naveed, K., Naqvi, S. S., Naveed, A. & Khan, T. M. (2023). Robust retinal blood vessel segmentation using a patch-based statistical adaptive multi-scale line detector. Digital Signal Processing: A Review Journal, 139, Article 104075. https://doi.org/10.1016/j.dsp.2023.104075

Liu, L., Grombacher, D., Griffiths, M., Vang, M. & Larsen, J. J. (2023). Signal Processing Steady-State Surface NMR Data. IEEE Transactions on Instrumentation and Measurement, 72, Article 6502313. https://doi.org/10.1109/TIM.2023.3264033

Vang, M., Grombacher, D., Griffiths, M. P., Liu, L. & Larsen, J. J. (2023). Technical note: High-density mapping of regional groundwater tables with steady-state surface nuclear magnetic resonance - three Danish case studies. Hydrology and Earth System Sciences, 27(16), 3115-3124. https://doi.org/10.5194/hess-27-3115-2023

Kakavandi, F., Gomes, C., de Reus, R., Badstue, J., Jensen, J. L., Larsen, P. G. & Iosifidis, A. (2023). Towards Developing a Digital Twin for a Manufacturing Pilot Line: An Industrial Case Study. In E. Karaarslan, Ö. Aydin, Ü. Cali & M. Challenger (Eds.), Digital Twin Driven Intelligent Systems and Emerging Metaverse (pp. 39-64). Springer. https://doi.org/10.1007/978-981-99-0252-1_2

Oleksiienko, I. (2023). Uncertainty Estimation for 3D Object Detection and Tracking. Aarhus University.

Dobbs, A. M., Ginn, D., Skovsen, S. K., Yadav, R., Jha, P., Bagavathiannan, M. V., Mirsky, S. B., Reberg-Horton, C. S. & Leon, R. G. (2023). Using structure-from-motion to estimate cover crop biomass and characterize canopy structure. Field Crops Research, 302, Article 109099. https://doi.org/10.1016/j.fcr.2023.109099

Laakom, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2023). WLD-Reg: A Data-Dependent Within-Layer Diversity Regularizer. In B. Williams, Y. Chen & J. Neville (Eds.), AAAI-23 Technical Tracks 7 (pp. 8421-8429). Article 190493 AAAI Press.

Oleksiienko, I. & Iosifidis, A. (2022). 3D object detection and tracking. In A. Iosifidis & A. Tefa (Eds.), Deep Learning for Robot Perception and Cognition (pp. 313-340). Elsevier. https://doi.org/10.1016/B978-0-32-385787-1.00018-X

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

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, Article e13837. https://doi.org/10.7717/peerj.13837

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

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. In 2022 IEEE International Smart Cities Conference (ISC2) IEEE. https://doi.org/10.1109/ISC255366.2022.9921939

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

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

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), Article 62. https://doi.org/10.3390/jimaging8030062

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. In Proceedings of the Symposium on the Application of Geophyics to Engineering and Environmental Problems, SAGEEP (pp. 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

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

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, Article 5919814. https://doi.org/10.1109/TGRS.2022.3202304

Mann, H. M. R., Iosifidis, A., Jepsen, J. U., Welker, J. M., Loonen, M. J. J. E. & Høye, T. T. (2022). Automatic flower detection and phenology monitoring using time-lapse cameras and deep learning. Remote Sensing in Ecology and Conservation, 8(6), 765-777. https://doi.org/10.1002/rse2.275

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

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

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

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. Paper presented at NSG2022 3rd Conference on Airborne, Drone and Robotic Geophysics, Beograd, Serbia. https://doi.org/10.3997/2214-4609.202220038

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