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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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, [105017]. https://doi.org/10.1016/j.cageo.2021.105017

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, [4503610]. https://doi.org/10.1109/TGRS.2021.3076121

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), [62]. https://doi.org/10.3390/jimaging8030062

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

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

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, [e13837]. https://doi.org/10.7717/peerj.13837

Khare, S. K., Gaikwad, N. & Bokde, N. D. (2022). An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets. Sensors (Switzerland), 22(21), [8128]. https://doi.org/10.3390/s22218128

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, [5919814]. https://doi.org/10.1109/TGRS.2022.3202304

Lillelund, C. M., Jensen, H. B. & Pedersen, C. F. (2022). Cloud K-SVD for Image Denoising. SN Computer Science, 3, [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, [109150]. https://doi.org/10.1016/j.comnet.2022.109150

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.. Proceedings of the Symposium on the Application of Geophyics to Engineering and Environmental Problems, SAGEEP Bind 2022-March

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

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, [102719]. https://doi.org/10.1016/j.mechatronics.2021.102719

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. I A-L. Andersen, R. Andersen, T. D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (red.), 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 (s. 224-232). Springer. Lecture Notes in Mechanical Engineering https://doi.org/10.1007/978-3-030-90700-6_25

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

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

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, [8024005]. https://doi.org/10.1109/LGRS.2021.3118064

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

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, [8395]. https://doi.org/10.1038/s41598-022-12167-8

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

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

Gaikwad, N. B., Khare, S. K., Satpute, N. & Keskar, A. G. (2022). Hardware Implementation of High-performance Classifiers for Edge Gateway of Smart Automobile. I Proceedings of PCEMS 2022 - 1st International Conference on the Paradigm Shifts in Communication, Embedded Systems, Machine Learning and Signal Processing (s. 74-77). Institute of Electrical and Electronics Engineers Inc.. Proceedings of PCEMS 2022 - 1st International Conference on the Paradigm Shifts in Communication, Embedded Systems, Machine Learning and Signal Processing https://doi.org/10.1109/PCEMS55161.2022.9808049

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

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

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

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

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, [106277]. https://doi.org/10.1016/j.compbiomed.2022.106277

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

Heidari, N. (2022). Representation Learning for Graph-Structured Data Analysis. Aarhus Universitet.

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

Gislum, R., Jørgensen, R., Mortensen, A. K. & Mikkelsen, B. (2022). Roboter im Ackerbau - Entwicklung aus der Nische. 39. Abstract fra DLG-Technikertagung 2022.

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

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

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 fra The 8th International Workshop on Magnetic Resonance Sounding, Strasbourg, Frankrig. https://mrs2021.sciencesconf.org/data/pages/proceedings_MRS2021_distrib_v2.pdf

Khare, S. K., Gaikwad, N. & Bajaj, V. (2022). VHERS: A Novel Variational Mode Decomposition and Hilbert Transform-Based EEG Rhythm Separation for Automatic ADHD Detection. IEEE Transactions on Instrumentation and Measurement, 71, 1-10. [4008310]. https://doi.org/10.1109/TIM.2022.3204076

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

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), [103169]. https://doi.org/10.1016/j.bspc.2021.103169

Nguyen, A. Q., Nguyen, H. T., Tran, V. C., Pham, H. X. & Pestana, J. (2021). A Visual Real-time Fire Detection using Single Shot MultiBox Detector for UAV-based Fire Surveillance. I S. Bahk, P. Tran-Gia, J. Van der Spiegel & N. X. Quynh (red.), 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE) (s. 338-343). IEEE. https://doi.org/10.1109/ICCE48956.2021.9352080

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), [270]. https://doi.org/10.3390/jimaging7120270

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

Naveed, K., Akhtar, M. T., Siddiqui, M. F. & Rehman, N. U. (2021). A statistical approach to signal denoising based on data-driven multiscale representation. Digital Signal Processing, 108, [102896]. https://doi.org/10.1016/j.dsp.2020.102896

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

Ahmadi, H., Rafiei Foroushani, M., Afshari-Igder, M., Gheisarnejad Chirani, M. & Khooban, M. H. (2021). An Energy Efficient Solution for Fuel Cell Heat Recovery in Zero-Emission Ferry Boats: Deep Deterministic Policy Gradient. I E E E Transactions on Vehicular Technology, 70(8), 7571-7581. https://doi.org/10.1109/TVT.2021.3094899.

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), [343]. https://doi.org/10.3390/s21020343

Gislum, R., Thomsen, I. K., Hansen, E. M., Mortensen, A. K., Larsen, R. & Olesen, J. E., (2021). Analyser i pilotprojekt om biomasse på baggrund af data fra forsøgsår 2020, Nr. 2021-0206943, 18 s., maj 05, 2021. Rådgivningsnotat fra DCA – National Center for Fødevarer og Jordbrug

Oleksiienko, I. & Iosifidis, A. (2021). Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems. I N. Mallenahalli, A. Bhattacharya, S. Senatore, A. Negi & A. Hirose (red.), 2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) (s. 59-64). IEEE. https://doi.org/10.1109/ICETCI51973.2021.9574075

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

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

Tan, S., Mortensen, A. K., Ma, X., Boelt, B. & Gislum, R. (2021). Assessment of grass lodging using texture and canopy height distribution features derived from UAV visual-band images. Agricultural and Forest Meteorology, 308-309, [108541]. https://doi.org/10.1016/j.agrformet.2021.108541

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AU Engineering