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Institut for Elektro- og Computerteknologi

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

Sortér efter: Dato | Forfatter | Titel

Bjerge, K. & Larsen, P. G. (2013). Using VDM in a Co-Simulation Setting for an Industrial Conveyor System. I The Modelling, Identification and Control - MIC 2013 conference ACTA Press. https://doi.org/10.2316/P.2013.794-005

Bjerge, K., Fynbo, H. O. U. & Johansen, J. G. (2019). A system on programmable chip design of a digitizer with improved trapezoidal filter validation. Microprocessors and Microsystems, 65, 7-13. https://doi.org/10.1016/j.micpro.2018.12.004

Bjerge, K., Frigaard, C. E., Mikkelsen, P. H., Nielsen, T. H., Misbih, M. & Kryger, P. (2019). A computer vision system to monitor the infestation level of Varroa destructor in a honeybee colony. Computers and Electronics in Agriculture, 164(September), Artikel 104898. https://doi.org/10.1016/j.compag.2019.104898

Bjerge, K., Nielsen, J. B., Sepstrup, M. V., Helsing-Nielsen, F. & Høye, T. T. (2020). A light trap and computer vision system to detect and classify live moths(Lepidoptera) using tracking and deep learning.

Bjerge, K., Nielsen, J. B., Sepstrup, M. V., Helsing-Nielsen, F. & Høye, T. T. (2021). An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning. Sensors (Switzerland), 21(2), Artikel 343. https://doi.org/10.3390/s21020343

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, Artikel 104363. https://doi.org/10.1016/j.micpro.2021.104363

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

Bjerge, K., Alison, J., Dyrmann, M., Frigaard, C. E., Mann, H. M. R. & Høye, T. T. (2022). Accurate detection and identification of insects from camera trap images with deep learning. bioRxiv. https://doi.org/10.1101/2022.10.25.513484

Bjerge, K., Frigaard, C. E. & Karstoft, H. (2022). Motion Informed Object Detection of Small Insects in Time-lapse Camera Recordings. arxiv.org. https://arxiv.org/abs/2212.00423

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

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. bioRxiv. https://doi.org/10.1101/2023.06.29.546989

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), Artikel e0000051. https://doi.org/10.1371/journal.pstr.0000051

Bjerge, K., Pinoy, N., Karstoft, H. & Høye, T. T. (2023). Field trials with automated moth monitoring in three different habitats of Denmark. Abstract fra 2023 Butterfly Conservation Symposium, Bedfordshire , Storbritannien.

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, Artikel 102278. https://doi.org/10.1016/j.ecoinf.2023.102278

Bjerge, K., Bodesheim, P. & Karstoft, H. (2024). Few-Shot Learning with Novelty Detection. 340-363. Afhandling præsenteret på DeLTA 2024 - 5th International Conference on Deep Learning Theory and Applications. https://doi.org/10.1007/978-3-031-66694-0_21

Bjerge, K., Karstoft, H., Mann, H. M. R. & Høye, T. T. (2024). A deep learning pipeline for time-lapse camera monitoring of floral environments and insect populations. bioRxiv. https://doi.org/10.1101/2024.04.12.589205

Bjerge, K., Karstoft, H. & Høye, T. T. (2024). Towards edge processing of images from insect camera traps. bioRxiv. https://doi.org/10.1101/2024.07.01.601488

Bjerge, K., Karstoft, H., Mann, H. M. R. & Høye, T. T. (2024). A deep learning pipeline for time-lapse camera monitoring of insects and their floral environments. Ecological Informatics, 84, Artikel 102861. https://doi.org/10.1016/j.ecoinf.2024.102861

Bjerge, K., Bodesheim, P. & Karstoft, H. (2025). Deep Image Clustering with Model-Agnostic Meta-Learning. I In Proceedings of the 20th International Joint Conference on Computer Vision (Bind 2, s. 286-297). Imaging and Computer Graphics Theory and Applications - VISAPP 2025. https://doi.org/10.5220/0013114600003912

Bjerge, K., Karstoft, H. & Høye, T. T. (2025). Towards edge processing of images from insect camera traps. Remote Sensing in Ecology and Conservation, 11(5), 573-589. https://doi.org/10.1002/rse2.70007

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

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/

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

Beevi, F. H. A., Miranda, J., Pedersen, C. F. & Wagner, S. R. (2015). An Evaluation of Commercial Pedometers for Monitoring Slow Walking Speed Populations. Telemedicine and e-Health, 22(5). https://doi.org/10.1089/tmj.2015.0120

Beevi, F. H. A., Wagner, S. R., Hallerstede, S. & Pedersen, C. F. (2016). Data Quality Oriented Taxonomy of Ambient Assisted Living Systems. I IET International Conference on Technologies for Active and Assisted Living: TechAAL, 2015 Institution of Engineering and Technology. https://doi.org/10.1049/ic.2015.0140

Beevi, F. H. A., Wagner, S. R., Pedersen, C. F. & Hallerstede, S. (2016). Data Quality Oriented Efficacy Evaluation Method for Ambient Assisted Living Technologies. I PervasiveHealth 2016 - 10th EAI International Conference on Pervasive Computing Technologies for Healthcare Association for Computing Machinery. https://doi.org/10.4108/eai.16-5-2016.2263753

Barzegar Gerdroodbary, M. & Salavatidezfouli, S. (2025). A predictive surrogate model based on linear and nonlinear solution manifold reduction in cardiovascular FSI: A comparative study. Computers in Biology and Medicine, 189, Artikel 109959. https://doi.org/10.1016/j.compbiomed.2025.109959

Barzegar Gerdroodbary, M. & Salavatidezfouli, S. (2025). A predictive surrogate model of blood haemodynamics for patient-specific carotid artery stenosis. Journal of the Royal Society Interface, 22(224), Artikel 20240774. https://doi.org/10.1098/rsif.2024.0774

Barfod, A. A. S., Lévy, L. & Larsen, J. J. (2021). Automatic processing of time domain induced polarization data using supervised artificial neural networks. Geophysical Journal International, 224(1), 312-325. https://doi.org/10.1093/gji/ggaa460

Banaei, M., Rafiei Foroushani, M., Boudjadar, J. & Khooban, M. H. (2020). A Comparative Analysis of Optimal Operation Scenarios in Hybrid Emission-Free Ferry Ships. IEEE Transactions on Transportation Electrification, 6(1), 318-333. Artikel 8976275. https://doi.org/10.1109/TTE.2020.2970674

Banaei, M., Ghanami, F., Rafiei Foroushani, M., Boudjadar, J. & Khooban, M. H. (2020). Energy Management of Hybrid Diesel/Battery Ships in Multidisciplinary Emission Policy Areas. Energies, 13(16), Artikel 4179. https://doi.org/10.3390/en13164179

Bahner , K., Larsen, J. J. & Auken, E. (2015). Multichannel magnetic resonance sounding with wirelessly operated coils – A design study. Poster-session præsenteret på The 6th International Workshop on Magnetic Resonance, Aarhus , Danmark. http://hgg.au.dk/fileadmin/www.dna18.au.dk/MRS2015_program.pdf

Bækkegaard, S., Blixenkrone-Møller, J., Larsen, J. J. & Jochumsen, L. (2018). Target Classiﬁcation Using Kinematic Data and a Recurrent Neural Network. I H. Rohling (red.), 2018 19th International Radar Symposium, IRS 2018: Proceedings (Bind 2018). Artikel 8448118 IEEE. https://doi.org/10.23919/IRS.2018.8448118

Bäckström, T., Pedersen, C. F., Fischer, J., Hüttenberger, M. & Alfonso, P. (2015). Concept for Encoding of Information. (Patentnummer EP20140158396).

Bäckström, T., Pedersen, C. F., Fischer, J. & Pietrzyk, G. (2015). Finding Line Spectral Frequencies Using the Fast Fourier Transform. I Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing: ICASSP (s. 5122-5126). IEEE. https://doi.org/10.1109/ICASSP.2015.7178947

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

Auken, E., Foged, N., Larsen, J. J., Lassen, K. V. T., Maurya, P. K., Møller Dath, S. & Eiskjær, T. T. (2019). tTEM — A towed transient electromagnetic system for detailed 3D imaging of the top 70 m of the subsurface. Geophysics, 84(1), E13-E22. https://doi.org/10.1190/geo2018-0355.1

Aslund, S., Jensen, K. & Jørgensen, R. N. (2011). Complete coverage path planning of a random polygon - A FroboMind component. http://www.njf.nu/site/seminarRedirect.asp?intSeminarID=441&p=1004

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., Hussain, S., Sadiq Fareed, M. & Khan, S. (2017). Multinational vehicle license plate detection in complex backgrounds. Journal of Visual Communication and Image Representation, 46, 176-186. https://doi.org/10.1016/j.jvcir.2017.03.020

Asif, M. R., Qi, C., Hussain, S. & Sadiq Fareed, M. (2016). Multiple License Plate Detection for Chinese Vehicles in Dense Traffic Scenarios. IET Intelligent Transport Systems, 10(8). https://doi.org/10.1049/iet-its

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

Asif, M. R., Bording, T. S., Barfod, A. A. S., Zhang, B., Larsen, J. J. & Auken, E. (2020). Improving computational efficiency of forward modelling for ground-based time-domain electromagnetic data using neural networks. Abstract fra EGU:General assembly 2020 , Vienna , Østrig. https://doi.org/10.5194/egusphere-egu2020-7067

Asif, M. R., Bording, T. S., Barfod, A. S., Auken, E. & Larsen, J. J. (2020). Effect of Data Normalization on Neural Networks for the Forward Modelling of Transient Electromagnetic Data,. Abstract fra Near Surface Geoscience 2020.

Asif, M. R., Bording, T. S., Barfod, A. S., Grombacher, D. J., Maurya, P. K., Christiansen, A. V., Auken, E. & Larsen, J. J. (2021). Effect of Data Pre-Processing on the Performance of Neural Networks for 1-D Transient Electromagnetic Forward Modeling. IEEE Access, 9, 34635-34646. https://doi.org/10.1109/ACCESS.2021.3061761

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, Artikel 4503610. https://doi.org/10.1109/TGRS.2021.3076121

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 fra AGU 2021, USA.

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..

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

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

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Revideret 13.11.2025

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AU Engineering