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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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

Naveed, K., Iqbal, J. & Ur Rehman, H. (2012). Brain controlled human robot interface. Proceedings of 2012 International Conference on Robotics and Artificial Intelligence, 55-60. https://doi.org/10.1109/ICRAI.2012.6413410

Naveed, K., Ali, B. & Akhtar, M. T. (2016). A variable step size affine projection algorithm based on the principle of orthogonality. Midwest Symposium on Circuits and Systems. https://doi.org/10.1109/MWSCAS.2016.7870057

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

Naveed, K., Abdullah, F., Madni, H. A., Khan, M. A. U., Khan, T. M. & Naqvi, S. S. (2021). Towards automated eye diagnosis: An improved retinal vessel segmentation framework using ensemble block matching 3d filter. Diagnostics, 11(1), Artikel 114. https://doi.org/10.3390/diagnostics11010114

Shah, A., Naqvi, S. S., Naveed, K., Salem, N., Khan, M. A. U. & Alimgeer, K. S. (2021). Automated Diagnosis of Leukemia: A Comprehensive Review. IEEE Access, 9, 132097-132124. https://doi.org/10.1109/ACCESS.2021.3114059

Raza, M., Naveed, K., Akram, A., Salem, N., Afaq, A., Madni, H. A., Khan, M. A. U. & Mui-Zzud-din (2021). DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images. PLOS ONE, 16(12), Artikel e0261698. https://doi.org/10.1371/journal.pone.0261698

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

Naveed, K., Ehsan, S., McDonald-Maier, K. D. & Ur Rehman, N. (2019). A multiscale denoising framework using detection theory with application to images from CMOS/CCD sensors. Sensors (Switzerland), 19(1), Artikel 206. https://doi.org/10.3390/s19010206

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

ur Rehman, N., Ehsan, S., Abdullah, S. M. U., Akhtar, M. J., Mandic, D. P. & McDonald-Maier, K. D. (2015). Multi-Scale pixel-based image fusion using multivariate empirical mode decomposition. Sensors (Switzerland), 15(5), 10923-10947. https://doi.org/10.3390/s150510923

Rehman, N. U. & Mandic, D. P. (2009). Qualitative analysis of rotational modes within three dimensional empirical mode decomposition. I 2009 IEEE International Conference on Acoustics, Speech and Signal Processing IEEE. https://doi.org/10.1109/icassp.2009.4960367

Looney, D., Rehman, N. U., Mandic, D., Rutkowski, T. M., Heidenreich, A. & Beyer, D. (2009). Conditioning multimodal information for smart environments. I 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC) IEEE. https://doi.org/10.1109/icdsc.2009.5289373

Rehman, N., Looney, D., Rutkowski, T. M. & Mandic, D. P. (2009). Bivariate EMD-based image fusion. I 2009 IEEE/SP 15th Workshop on Statistical Signal Processing IEEE. https://doi.org/10.1109/ssp.2009.5278639

Rehman, N. U. & Mandic, D. P. (2010). Quadrivariate Empirical Mode Decomposition. I The 2010 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/ijcnn.2010.5596768

Rehman, N. U. & Mandic, D. P. (2011). Filter Bank Property of Multivariate Empirical Mode Decomposition. IEEE Transactions on Signal Processing, 59(5), 2421-2426. https://doi.org/10.1109/tsp.2011.2106779

Park, C., Looney, D., Rehman, N. U., Ahrabian, A. & Mandic, D. P. (2013). Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(1), 10-22. https://doi.org/10.1109/tnsre.2012.2229296

Ahrabian, A., Rehman, N. U. & Mandic, D. (2013). Bivariate Empirical Mode Decomposition for Unbalanced Real-World Signals. IEEE Signal Processing Letters, 20(3), 245-248. https://doi.org/10.1109/lsp.2013.2242062

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

Mandic, D. P., Rehman, N. U., Wu, Z. & Huang, N. E. (2013). Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis. IEEE Signal Processing Magazine, 30(6), 74-86. https://doi.org/10.1109/msp.2013.2267931

Rehman, N. U. & Mandic, D. P. (2014). Dynamically-Sampled Bivariate Empirical Mode Decomposition. IEEE Signal Processing Letters, 21(7), 857-861. https://doi.org/10.1109/lsp.2014.2317773

Li, L., Looney, D., Park, C., Rehman, N. U. & Mandic, D. P. (2011). Power independent EMG based gesture recognition for robotics. I 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE. https://doi.org/10.1109/iembs.2011.6090036

Rehman, N. U., Xia, Y. & Mandic, D. P. (2010). Application of multivariate empirical mode decomposition for seizure detection in EEG signals. I 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology IEEE. https://doi.org/10.1109/iembs.2010.5626665

Hemakom, A., Ahrabian, A., Looney, D., Rehman, N. U. & Mandic, D. P. (2015). Nonuniformly sampled trivariate empirical mode decomposition. I 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) IEEE. https://doi.org/10.1109/icassp.2015.7178660

Rehman, N., Ehsan, S., Naveed, K., McDonald-Maier, K. D. & Safdar, M. W. (2015). Dynamically sampled multivariate empirical mode decomposition. Electronics Letters, 51(24), 2049-2051. https://doi.org/10.1049/el.2015.1176

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

HAO, H., WANG, H., REHMAN, N. UR. & TIAN, H. (2016). A Study of the Characteristics of MEMD for Fractional Gaussian Noise. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E99.A(6), 1228-1232. https://doi.org/10.1587/transfun.e99.a.1228

Abdullah, S. M. U., Rehman, N. U., Khan, M. M. & Mandic, D. P. (2015). A Multivariate Empirical Mode DecompositionBased Approach to Pansharpening. IEEE Transactions on Geoscience and Remote Sensing, 53(7), 3974-3984. https://doi.org/10.1109/tgrs.2015.2388497

Ehsan, S., Clark, A., Rehman, N. & McDonald-Maier, K. (2015). Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems. Sensors, 15(7), 16804-16830. https://doi.org/10.3390/s150716804

Ferrarini, B., Ehsan, S., Rehman, N. U., Leonardis, A. & McDonald-Maier, K. D. (2016). Automatic Selection of the Optimal Local Feature Detector. I Lecture Notes in Computer Science (s. 284-289). Springer International Publishing. https://doi.org/10.1007/978-3-319-41501-7_32

Rehman, N. U., Naveed, K., Ehsan, S. & McDonald-Maier, K. (2016). Multi-scale image denoising based on goodness of fit (GOF) tests. I 2016 24th European Signal Processing Conference (EUSIPCO) IEEE. https://doi.org/10.1109/eusipco.2016.7760508

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Revideret 10.08.2023

AU Engineering