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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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. 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. 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. I 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. I 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. I 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. I 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. 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

Laakom, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2021). On Feature Diversity in Energy-based models. Afhandling præsenteret på 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. Afhandling præsenteret på 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. Afhandling præsenteret på 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. Afhandling præsenteret på 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, Artikel 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. I 2021 International Balkan Conference on Communications and Networking, BalkanCom 2021 (s. 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

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

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. I 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, Artikel 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. I C. R. Martin, M. J. Blas & A. I. Psijas (red.), 2021 Annual Modeling and Simulation Conference (ANNSIM) (s. 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. I 2021 IEEE 17th International Conference on Automation Science and Engineering, CASE 2021 (s. 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. I 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. I 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. I 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. I 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. I 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE) (s. 1901-1907) https://doi.org/10.1109/CASE49439.2021.9551637

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

Nielsen, A. H., Iosifidis, A. & Karstoft, H. (2021). IrradianceNet: Spatiotemporal Deep Learning Model for Satellite-Derived Solar Irradiance Nowcasting. Solar Energy, 228, 659-669. https://doi.org/10.1016/j.solener.2021.09.073

Naveed, K., Mukhtar, S. & Ur Rehman, N. (2021). Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis. I 2021 IEEE Statistical Signal Processing Workshop, SSP 2021 (s. 441-445). IEEE. https://doi.org/10.1109/SSP49050.2021.9513823

Pham, X. H., Bozcan, I., Sarabakha, A., Haddadin, S. & Kayacan, E. (2021). GateNet: An Efficient Deep Neural Network Architecture for Gate Perception Using Fish-Eye Camera in Autonomous Drone Racing. Afhandling præsenteret på 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, Prag, Tjekkiet. https://sarabakha.info/files/papers/conference/IROS_2021.pdf

Mehndiratta, M., Camci, E. & Kayacan, E. (2021). Can deep models help a robot to tune its controller? A step closer to self-tuning model predictive controllers. Electronics (Switzerland), 10(18), Artikel 2187. https://doi.org/10.3390/electronics10182187

Dyrmann, M., Mortensen, A. K., Linneberg, L., Høye, T. T. & Bjerge, K. (2021). Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning. Sensors (Switzerland), 21(18), Artikel 6126. https://doi.org/10.3390/s21186126

Heidari, N. & Iosifidis, A. (2021). Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition. I ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (Bind 2021-June, s. 3220-3224). IEEE. https://doi.org/10.1109/ICASSP39728.2021.9413860

Heidari, N. & Iosifidis, A. (2021). Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition. I Proceedings of ICPR 2020 - 25th International Conference on Pattern Recognition (s. 7907-7914). Artikel 9412091 IEEE. https://doi.org/10.1109/ICPR48806.2021.9412091

Heidari, N. & Iosifidis, A. (2021). Progressive Graph Convolutional Networks for Semi-Supervised Node Classification. IEEE Access, 9, 81957-81968. https://doi.org/10.1109/ACCESS.2021.3085905

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, Artikel 108541. https://doi.org/10.1016/j.agrformet.2021.108541

Dormann, F., Frisk, O., Andersen, L. N. & Pedersen, C. F. (2021). Not All Noise Is Accounted Equally: How Differentially Private Learning Benefits From Large Sampling Rates. I 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021 IEEE. https://doi.org/10.1109/MLSP52302.2021.9596307

Lillelund, C. M., Pedersen, C. F., Harbo, M. & Fibiger Smed, C. (2021). Vejen til genoptræning går gennem data. Medicoteknik, 8(3), 18-19. https://ipaper.ipapercms.dk/TechMedia/Medicoteknik/2021/3/?page=18

Kiranyaz, S., Malik, J., Abdallah, H. B., Ince, T., Iosifidis, A. & Gabbouj, M. (2021). Exploiting heterogeneity in operational neural networks by synaptic plasticity. Neural Computing and Applications, 33(13), 7997-8015. https://doi.org/10.1007/s00521-020-05543-w

Ugurlu, H. I., Kalkan, S. & Saranli, A. (2021). Reinforcement Learning versus Conventional Control for Controlling a Planar Bi-rotor Platform with Tail Appendage. Journal of Intelligent and Robotic Systems: Theory and Applications, 102(4), Artikel 77. https://doi.org/10.1007/s10846-021-01412-3

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

Lati, R. N., Rasmussen, J., Andujar, D., Dorado, J., Berge, T. W., Wellhausen, C., Pflanz, M., Nordmeyer, H., Schirrmann, M., Eizenberg, H., Neve, P., Jørgensen, R. N. & Christensen, S. (2021). Site-specific weed management—constraints and opportunities for the weed research community: Insights from a workshop. Weed Research, 61(3), 147-153. https://doi.org/10.1111/wre.12469

Kiranyaz, S., Malik, J., Abdallah, H. B., Ince, T., Iosifidis, A. & Gabbouj, M. (2021). Self-organized Operational Neural Networks with Generative Neurons. Neural Networks, 140, 294-308. https://doi.org/10.1016/j.neunet.2021.02.028

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