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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Zullo, H., Cline, K., Parker, M., Buckmire, R., George, J., Gurski, K., Larsen, J. J., Mellor, B. & Oberweiser, J. (2011). Student Surveys: What Do They Think? I K. Cline & H. Zullo (red.), Teaching Mathematics with Classroom Voting, With and Without Clicker (s. 29-34). Mathematical Association of America. http://www.maa.org/publications/ebooks/teaching-mathematics-with-classroom-voting-with-and-without-clickers

Zhou, S., Helwa, M. K., Schoellig, A. P., Sarabakha, A. & Kayacan, E. (2019). Knowledge transfer between robots with similar dynamics for high-accuracy impromptu trajectory tracking. I 2019 18th European Control Conference, ECC 2019 Artikel 8796140 IEEE. https://doi.org/10.23919/ECC.2019.8796140

Zhang, C., Rossi, C. & Kayacan, E. (2017). Interval Type-2 Fuzzy-Neuro Control of Nonlinear Systems With Proved Overall System Stability. I 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 (s. 1-6). Artikel 8015495 IEEE. https://doi.org/10.1109/FUZZ-IEEE.2017.8015495

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), Artikel 013009. https://doi.org/10.1103/PRXEnergy.2.013009

Zahra, A., Kanwal, N., Rehman, N. U., Ehsan, S. & McDonald-Maier, K. D. (2017). Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition. Computers in Biology and Medicine, 88, 132-141. https://doi.org/10.1016/j.compbiomed.2017.07.010

Yan, R.-J., Wu, J., Yeung Lee, J., Manan Khan, A., Han, C.-S., Kayacan, E. & Chen, I.-M. (2016). A Novel Method for 3D Reconstruction: Division and Merging of Overlapping B-spline surfaces. Computer-Aided Design, 81, 14-23. https://doi.org/10.1016/j.cad.2016.08.007

Yan, R.-J., Kayacan, E., Chen, I.-M. & Kong Tiong, L. (2017). A novel building post-construction quality assessment robot: Design and prototyping. I IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems (s. 6020-6026). Artikel 8206499 IEEE. https://doi.org/10.1109/IROS.2017.8206499

Yan, R.-J., Leong Low, C., Duan, J., Liu, L., Kayacan, E., Chen, I.-M. & Tiong, R. (2016). Development of a Novel Post-Construction Quality Assessment Robot System. I 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016 (s. 1-6). Artikel 7838741 IEEE . https://doi.org/10.1109/ICARCV.2016.7838741

Yan, R. J., Kayacan, E., Chen, I. M., Tiong, L. K. & Wu, J. (2019). QuicaBot: Quality Inspection and Assessment Robot. IEEE Transactions on Automation Science and Engineering, 16(2), 506-517. https://doi.org/10.1109/TASE.2018.2829927

Xu, L., Iosifidis, A. & Gabbouj, M. (2018). Weighted Linear Discriminant Analysis Based on Class Saliency Information. I 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings: Proceedings (s. 2306-2310). Artikel 8451614 IEEE. https://doi.org/10.1109/ICIP.2018.8451614

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

Wilson Lee, Y. J., Mehndiratta, M. & Kayacan, E. (2018). Fly without borders with Additive Manufacturing: A microscale tilt-rotor Tricopter Design. I Proceedings of the 3rd International Conference on Progress in Additive Manufacturing (Pro-AM 2018) (Bind 2018-May, s. 256-261) https://doi.org/10.25341/D43K5G

Wewer, C. R. & Iosifidis, A. (2023). Improving Online non-destructive Moisture Content Estimation using Data Augmentation by Feature Space Interpolation with Variational Autoencoders. I 2023 IEEE 21st International Conference on Industrial Informatics (INDIN) IEEE. https://doi.org/10.1109/INDIN51400.2023.10218063

Westh Nicolajsen, H., Ahola, T., Fleury, A. M., Milkova Ilieva, T., Eg Larsen, J., Larsen, L. B., G. Nikolakopoulos, I., Z. Patrikakis, C., Pedersen, C. F., Roswall, R., Schultz, N. & Tolstrup Sørensen, L. (2007). Defining Usability of PN Services. Information Society Technologies, IST-FP6-IP-027396, My Personal Adaptive Global NET (MAGNET) Beyond, EU.

Waris, M. A., Iosifidis, A. & Gabbouj, M. (2017). CNN-based Edge Filtering for Object Proposals. Neurocomputing, 266, 631-640. https://doi.org/10.1016/j.neucom.2017.05.071

Waris, M. A., Iosifidis, A. & Gabbouj, M. (2016). Object Proposals using CNN-based edge filtering. I 2016 23rd International Conference on Pattern Recognition, ICPR 2016 (s. 627-632). Artikel 7899704 IEEE. https://doi.org/10.1109/ICPR.2016.7899704

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, Artikel 106698. https://doi.org/10.1016/j.engappai.2023.106698

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

Wagner, S. R., Hansen, F. O., Pedersen, C. F., Memon, M., Beevi, F. H. A., Mathissen, M., Nielsen, C. & Wesby, O. L. (2013). CareStore Platform for Seamless Deployment of Ambient Assisted Living Applications and Devices. I Pervasive Computing Technologies for Healthcare (PervasiveHealth) 2013: 7th International Conference on (s. 240-243). IEEE. https://doi.org/10.4108/icst.pervasivehealth.2013.252110

Wagner, S. R., Stenner, R., Memon, M., Beevi, F. H. A. & Pedersen, C. F. (2014). Common Ambient Assisted Living Home Platform for Seamless Care. Abstract fra 8th International Conference on Pervasive Computing Technologies for Healthcare, Oldenburg, Tyskland.

Wagner, S. R., Beevi, F. H. A., Pedersen, C. F., Miranda, J., Cabral, J. & Ravelo, B. (2016). Evaluation of Caregivers' First Time Use of a Seamless Deployment Platform. I Proceedings of the International Conference on E-Health, EH 2016 - Part of the Multi Conference on Computer Science and Information Systems 2016 (s. 59-66). IADIS.

V. Topalev, A., Oniz, Y., Kayacan, E. & Kaynak, O. (2011). Neuro-Fuzzy Control of Antilock Braking System Using Sliding Mode Incremental Learning Algorithm. Neurocomputing, 74(11), 1883-1893. https://doi.org/10.1016/j.neucom.2010.07.035

V. Topalev, A., G. Shakev, N., Kaynak, O. & Kayacan, E. (2010). Neuro-adaptive Approach for Controlling a Quad-rotor Helicopter Using Sliding Mode Learning Algorithm. I IFAC Proceedings Volumes (10 udg., Bind 43, s. 94-99). Elsevier. https://doi.org/10.3182/20100826-3-TR-4015.00020

V. Topalev, A., Kayacan, E., Oniz, Y. & Kaynak, O. (2009). Neuro-Fuzzy Control of Antilock Braking System Using Variable-Structure-Systems-based Learning Algorithm. I Adaptive and Intelligent Systems, 2009. ICAIS '09. International Conference on (s. 166-171). IEEE . https://doi.org/10.1109/ICAIS.2009.35

V. Topalev, A., Kayacan, E., Oniz, Y. & Kaynak, O. (2009). Adaptive Neuro-Fuzzy Control with Sliding Mode Learning Algorithm: Application to Antilock Braking System. I Asian Control Conference, 2009. ASCC 2009. 7th (s. 784-789). IEEE . https://ieeexplore.ieee.org/document/5276234/

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

Van Beers, R., Gutierrez, L. L., Schenk, A., Nicolai, B., Kayacan, E. & Saeys, W. (2014). Optical measurement techniques for the ripeness determination of Braeburn apples. I Proceedings International Conference of Agricultural Engineering (s. 1-7) http://www.geyseco.es/geystiona/adjs/comunicaciones/304/C02000001.pdf

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

Ugurlu, H. I., Pham, X. H. & Kayacan, E. (2022). Sim-to-Real Deep Reinforcement Learning for Safe End-to-End Planning of Aerial Robots. Robotics, 11(5), Artikel 109. https://doi.org/10.3390/robotics11050109

Tzogka, C., Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2019). Less is more: Deep Learning using subjective annotations for sentiment analysis from social media. I 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019: Proceedings Artikel 8918792 IEEE. https://doi.org/10.1109/MLSP.2019.8918792

Tu, G. J., Karstoft, H., Pedersen, L. J. & Jørgensen, E. (2013). Foreground detection using loopy belief propagation. Biosystems Engineering, 116(1), 88-96. https://doi.org/10.1016/j.biosystemseng.2013.06.011

Tu, G. J., Karstoft, H., Pedersen, L. J. & Jørgensen, E. (2014). Segmentation of sows in farrowing pens. IET Image Processing, 8(1), 56-68. https://doi.org/10.1049/iet-ipr.2012.0734

Tu, G. J. & Karstoft, H. (2015). Logarithmic dyadic wavelet transform with its applications in edge detection and reconstruction. Applied Soft Computing, 26, 193-201. https://doi.org/10.1016/j.asoc.2014.09.044

Tu, G. J., Karstoft, H., Pedersen, L. J. & Jørgensen, E. (2015). Illumination and Reflectance Estimation with its Application in Foreground Detection. Sensors, 15(9), 21407-21426. https://doi.org/10.3390/s150921407

Tsapanos, N., Iosifidis, A. & Pitas, I. (2015). Mathematical preliminaries: graphs and matrices. I I. Pitas (red.), Graph-Based Social Media Analysis CRC Press.

Tsapanos, N., Tefas, A., Nikolaidis, N., Iosifidis, A. & Pitas, I. (2015). Fast Kernel Matrix Computation for Big Data Clustering. I Procedia Computer Science (s. 2445-2452). ELSEVIER. https://doi.org/10.1016/j.procs.2015.05.352

Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2017). Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks. I Proceedings - 2017 IEEE 19th Conference on Business Informatics, CBI 2017 (s. 7-12). Artikel 8010701 IEEE. https://doi.org/10.1109/CBI.2017.23

Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2017). Using Deep Learning to Detect Price Change Indications in Financial Markets. Proceedings of the European Signal Processing Conference, 2017, 2580-2584. https://doi.org/10.23919/EUSIPCO.2017.8081663

Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2020). Using Deep Learning for price prediction by exploiting stationary limit order book features. Applied Soft Computing Journal, 93, Artikel 106401. https://doi.org/10.1016/j.asoc.2020.106401

Tran, D. T., Iosifidis, A. & Gabbouj, M. (2018). Improving efficiency in convolutional neural networks with multilinear filters. Neural Networks, 105, 328-339. https://doi.org/10.1016/j.neunet.2018.05.017

Tran, D. T., Magris, M., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2018). Tensor representation in high-frequency financial data for price change prediction. I 2017 IEEE Symposium Series on Computational Intelligence: SSCI 2017 - Proceedings (Bind 2017, s. 1-7). IEEE. https://doi.org/10.1109/SSCI.2017.8280812

Tran, D. T., Iosifidis, A., Kanniainen, J. & Gabbouj, M. (2019). Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis. IEEE Transactions on Neural Networks and Learning Systems, 30(5), 1407 - 1418 . https://doi.org/10.1109/TNNLS.2018.2869225

Tran, D. T. & Iosifidis, A. (2019). Learning to Rank: A Progressive Neural Network Learning Approach. I 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (s. 8355-8359). Artikel 8683711 IEEE. https://doi.org/10.1109/ICASSP.2019.8683711

Tran, D. T., Kiranyaz, S., Gabbouj, M. & Iosifidis, A. (2019). PyGOP: A Python library for Generalized Operational Perceptron algorithms. Knowledge-Based Systems, 182, Artikel 104801. https://doi.org/10.1016/j.knosys.2019.06.009

Tran, D. T., Gabbouj, M. & Iosifidis, A. (2020). Performance Indicator in Multilinear Compressive Learning. I 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (s. 1822-1828). Artikel 9308418 IEEE. https://doi.org/10.1109/SSCI47803.2020.9308418

Tran, D. T., Yamac, M., Degerli, A., Gabbouj, M. & Iosifidis, A. (2021). Multilinear compressive learning. IEEE Transactions on Neural Networks and Learning Systems, 32(4), 1512-1524. Artikel 9070152. https://doi.org/10.1109/TNNLS.2020.2984831

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

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., 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

Tran, D. T., Gabbouj, M. & Iosifidis, A. (2022). Progressive and compressive learning. I A. Iosifidis & A. Tefas (red.), Deep Learning for Robot Perception and Cognition (s. 187-220). Elsevier. https://doi.org/10.1016/B978-0-32-385787-1.00014-2

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Revideret 10.08.2023

AU Engineering