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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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

Pedersen, C. F., Andersen, O. & Dalsgaard, P. (2009). ZZT-domain Immiscibility of the Opening and Closing Phases of the LF GFM under Frame Length Variations. Proceedings of the International Conference on Spoken Language Processing, 1099-1102.

Pedersen, C. F., Andersen, O. & Dalsgaard, P. (2009). ZZT-domain Immiscibility of the Opening and Closing Phases of the LF GFM under Frame Length Variations. Poster session presented at International Conference on Spoken Language Processing, Interspeech, Brighton, United Kingdom.

Pedersen, C. F. (2012). Z-transform Zeros in Mixed Phase Deconvolution of Speech. Aalborg University, Faculty of Engineering and Science, Dept. of Electronic Systems, Niels Jernes Vej 10, 9220 Aalborg East, Denmark.

Sarabakha, A. & Kayacan, E. (2016). Y6 tricopter autonomous evacuation in an Indoor Environment using Q-learning algorithm. In Decision and Control (CDC), 2016 IEEE 55th Conference on (pp. 5992-5997). IEEE . https://doi.org/10.1109/CDC.2016.7799189

Camci, E. & Kayacan, E. (2016). Y6 Waitress Quadcopter Explores How To Serve Drinks By Reinforcement Learning. In 2016 IEEE Region 10 Conference (TENCON) (pp. 28-32)

Laakom, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2021). Within-layer Diversity Reduces Generalization Gap. Paper presented at 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

Miranda, J., Cabral, J., Ravelo, B., Wagner, S. R., Pedersen, C. F., Memon, M. & Mathiesen, M. (2015). Wireless Authentication Platform for Healthcare Applications. In 2015 IEEE International Wireless Symposium (IWS) (pp. 1-4). IEEE. https://doi.org/10.1109/IEEE-IWS.2015.7164620

Miranda, J., Cabral, J., Ravelo, B., Wagner, S. R., Pedersen, C. F., Memon, M. & Mathiesen, M. (2015). Wireless Authentication Platform for Healthcare Applications. Poster session presented at IEEE MTT-S International Wireless Symposium, Shenzhen, China.

Steen, K. A., Therkildsen, O. R., Karstoft, H. & Green, O. (2012). Wildlife Communication: Electrical and Computer Engineering. Aarhus University, Department of Engineering. Technical report No. ECE-TR-7 https://pure.au.dk/portal/files/45572923/samlet_TCR_7_ver2.pdf

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

Xu, L., Iosifidis, A. & Gabbouj, M. (2018). Weighted Linear Discriminant Analysis Based on Class Saliency Information. In 2018 25th IEEE International Conference on Image Processing (ICIP): Proceedings (pp. 2306-2310). IEEE. https://doi.org/10.1109/ICIP.2018.8451614

Mortensen, A. K., Lisouski, P. & Ahrendt, P. (2016). Weight prediction of broiler chickens using 3D computer vision. Computers and Electronics in Agriculture, 123, 319-326. https://doi.org/10.1016/j.compag.2016.03.011

Dyrmann, M. (2014). Weed detection by UAV with camera guided landing sequence. Abstract from NJF Seminar, Lund, Sweden.

Naveed, K. & Rehman, N. U. (2020). Wavelet based multivariate signal denoising using Mahalanobis distance and EDF statistics. IEEE Transactions on Signal Processing, 68, 5997-6010. https://doi.org/10.1109/TSP.2020.3029659

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

Fu, C., Duan, R. & Kayacan, E. (2019). Visual tracking with online structural similarity-based weighted multiple instance learning. Information Sciences, 481, 292-310. https://doi.org/10.1016/j.ins.2018.12.080

Patrona, F., Iosifidis, A., Tefas, A., Nikolaidis, N. & Pitas, I. (2016). Visual Voice Activity Detection in the Wild. IEEE Transactions on Multimedia, 18(6), 967-977. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7420734

Patrona, F., Iosifidis, A., Tefas, A., Nikolaidis, N. & Pitas, I. (2015). Visual Voice Activity Detection based on Spatiotemporal Information and Bag of Words. In Image Processing (ICIP), 2015 IEEE International Conference on (pp. 2334-2338). IEEE. https://doi.org/10.1109/ICIP.2015.7351219

Iosifidis, A., Tefas, A. & Pitas, I. (2012). View-invariant action recognition based on Artificial Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 23(3), 412-424. https://doi.org/10.1109/TNNLS.2011.2181865

Iosifidis, A., Tefas, A. & Pitas, I. (2013). View-independent Human Action Recognition based on Multi-view Action Images and Discriminant Learning. In IVMSP Workshop, 2013 IEEE 11th IEEE. https://doi.org/10.1109/IVMSPW.2013.6611931

Steen, K. A., Therkildsen, O. R., Karstoft, H. & Green, O. (2012). Video-Based Detection of Goose Behaviours. Paper presented at Agriculture & Engineering for a Healthier Life, Valencia, Spain. http://cigr.ageng2012.org/images/fotosg/tabla_137_C0358.pdf

Mygdalis, V., Iosifidis, A., Tefas, A. & Pitas, I. (2014). Video summarization based on subclass Support Vector Data Description. In Computational Intelligence for Engineering Solutions (CIES), 2014 IEEE Symposium on IEEE. https://doi.org/10.1109/CIES.2014.7011849

Kourous, N., Iosifidis, A., Tefas, A., Nikolaidis, N. & Pitas, I. (2014). Video Characterization based on Activity Clustering. In Electrical and Computer Engineering (ICECE), 2014 International Conference on (pp. 266-269). IEEE. https://doi.org/10.1109/ICECE.2014.7026876

Jakobsen, M. O., Herskind, E. S., Bjerge, K., Ahrendt, P., Pedersen, C. F. & Knudsen, M. B. (2021). Vibration signatures in ball bearings as a function of lubricant viscosity ratio κ, under alternating lubrication conditions. Tribology International, 156, [106840]. https://doi.org/10.1016/j.triboint.2020.106840

Kayacan, E., Kayacan, E., Ramon, H. & Saeys, W. (2012). Velocity Control of a Spherical Rolling Robot Using a Grey-PID Type Fuzzy Controller With an Adaptive Step Size. In IFAC Proceedings Volumes (22 ed., Vol. 45, pp. 863-868). Elsevier. https://doi.org/10.3182/20120905-3-HR-2030.00123

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

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

Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2020). Variance-preserving Deep Metric Learning for Content-based Image Retrieval. Pattern Recognition Letters, 131, 8-14. https://doi.org/10.1016/j.patrec.2019.11.041

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

baran, E. A., Zhakypov, Z., Kayacan, E. & Sabanovic, A. (2014). Uzaktan Lazer Cerrahi İçin Kısıtlı Sistem Tabanlı Efendi Köle Denetleyicisi. In TOK 2014 (pp. 675-680) http://research.sabanciuniv.edu/24776/1/2014_RA417_TOK2014_Eray_205.pdf

Dalsgaard, P., Pedersen, C. F., Andersen, O. & Bayya, Y. (2008). Using zeros of the z-transform in the Analysis of Speech Signals. Poster session presented at Speech Analysis and Processing for Knowledge Discovery, Aalborg, Denmark.

Siboska, D. & Karstoft, H. (2015). Using illumination changes to synchronize eye tracking in visual paradigms. In G. Plantier, T. Schlutz, A. Fred & H. Gamboa (Eds.), Biomedical Engineering Systems and Technologies: 7th International Joint Conference, BIOSTEC 2014, Angers, France, March 3-6, 2014, Revised Selected Papers (Vol. 511, pp. 289-298). Communications in Computer and Information Science Vol. 511 https://doi.org/10.1007/978-3-319-26129-4_19

Dyrmann, M., Skovsen, S., Sørensen, R. A., Nielsen, P. R. & Nyholm Jørgensen, R. (2018). Using a fully convolutional neural network for detecting locations of weeds in images from cereal fields. Abstract from International Conference on Precision Agriculture, Montréal, Quebec, Canada.

Dyrmann, M., Skovsen, S., Laursen, M. S. & Nyholm Jørgensen, R. (2018). Using a fully convolutional neural network for detecting locations of weeds in images from cereal fields. In Proceedings of the 14th International Conference on Precision Agriculture International Society of Precision Agriculture. https://www.ispag.org/proceedings/?action=download&item=5081

Dalsgaard, P., Pedersen, C. F., Andersen, O. & Bayya, Y. (2008). Using Zeros of the z-transform in the Analysis of Speech Signals. In Proceedings of ISCA ITRW - Speech Analysis and Processing for Knowledge Discovery

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

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

Steen, K. A., Christiansen, P., Karstoft, H. & Nyholm Jørgensen, R. (2016). Using Deep Learning to Challenge Safety Standard for Highly Autonomous Machines in Agriculture. Journal of Imaging, 2(1), [6]. https://doi.org/10.3390/jimaging2010006

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, [106401]. https://doi.org/10.1016/j.asoc.2020.106401

Sørensen, L., Saugstrup, D., Schultz, N., Larsen, J., Jensen, K. L., Larsen, L. B., Pedersen, C. F., Kaldanis, V. & Vila, J. (2005). User centric scenarios for PNs of a valid architecture. Information Society Technologies, My Personal Adaptive Global NET (MAGNET), IST 507102.

Olesen, H., Jiang, B., Butkus, A., Schultz, N., Saugstrup, D., Sørensen, L., Pedersen, C. F., Jensen, J. J., Larsen, L. B., Björksten, M., Markopoulos, A., Gkanas, I., Kaldanis, V., Prasad, R., Alm, K., Vila, J. P. & Köstering, N. (2004). User Requirements for PN to Drive the Definition of a Valid Architecture. (1. udgave ed.). Information Society Technologies, 507102, My Personal Adaptive Global NET, d1.1.1b

Pedersen, J. S., Ahola, T., Alutoin, M., Karamolegkos, P., Larsen, L. B., Eg Larsen, J., G. Nikolakopoulos, I., Z. Patrikakis, C., Pedersen, C. F., Roswall, R., Schultz, N., Tolstrup Sørensen, L. & Tan, S-E. (2007). Usability of PN services (Low-Fi prototyping): D1.4.1. Information Society Technologies, IST-FP6-IP-027396, My Personal Adaptive Global NET (MAGNET) Beyond, EU.

Christiansen, P. H., Kragh, M. F., Brodskiy, Y. & Karstoft, H. (2019). UnsuperPoint: End-to-end Unsupervised Interest Point Detector and Descriptor. arXiv.org. https://arxiv.org/abs/1907.04011

Govdeli, Y., Moheed Bin Muzaffar, S., Raj, R., Elhadidi, B. & Kayacan, E. (2019). Unsteady aerodynamic modeling and control of pusher and tilt-rotor quadplane configurations. Aerospace Science and Technology, 94, [105421]. https://doi.org/10.1016/j.ast.2019.105421

D. Bedford, M., Gibson, D., Coggan, J., Siever, K., Wrana, A., Sobczak, D. & Alvarez Tunon, O. (2019). Ultrasonic Inspection of Flooded Mineshafts for Stability Monitoring. Mining Technology: Transactions of the Institutions of Mining and Metallurgy, 128(3), 177-185. https://doi.org/10.1080/25726668.2019.1607459

Bozcan, I. & Kayacan, E. (2020). UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 1158-1164). [9341790] IEEE. https://doi.org/10.1109/IROS45743.2020.9341790

Hansen, J. G., Heiß, M., Kozłowski, M. & Kayacan, E. (2022). UAV Trajectory Evaluation in Large Industrial Environments: A Cost-Effective Solution. In 2022 European Control Conference (ECC) (pp. 1336-1341). IEEE. https://doi.org/10.23919/ECC55457.2022.9838352

Kryger, P. & Bjerge, K. (2019). Tælling af varroamider. In Varroatolerance i Danmark : de første skridt februar 2019 (pp. 28-31). Danmarks Biavlerforening.

Kayacan, E. & Maslim, R. (2016). Type-2 Fuzzy Logic Trajectory Tracking Control of Quadrotor VTOL Aircraft With Elliptic Membership Functions. IEEE - ASME Transactions on Mechatronics, 22(1), 339-348. https://doi.org/10.1109/TMECH.2016.2614672

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Revised 03.10.2022

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