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Department of Electrical and Computer Engineering

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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Shahrak Nadimi, E., Andersson, K. J., Jørgensen, R. N., Maagaard, J., Mathiassen, S. K. & Christensen, S. (2009). Designing, modeling and controlling a novel autonomous laser weeding system. In Conference papers (pp. 1). World Congress on Computers in Agriculture.

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.

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)

Sarabakha, A. & Kayacan, E. (2016). Y6 tricopter autonomous evacuation in an Indoor Environment using Q-learning algorithm. In 2016 IEEE 55th Conference on Decision and Control, CDC 2016 (pp. 5992-5997). Article 7799189 IEEE . https://doi.org/10.1109/CDC.2016.7799189

Jensen, K., Jørgensen, R. N., Bøgild, A., Jørgensen, O. J., Nielsen, S. H. & Persson, R. B. (2011). Work in progress: Robotics mapping of landmine and UXO contaminated areas.

Laakom, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2023). WLD-Reg: A Data-Dependent Within-Layer Diversity Regularizer. In B. Williams, Y. Chen & J. Neville (Eds.), AAAI-23 Technical Tracks 7 (pp. 8421-8429). Article 190493 AAAI Press.

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), Article 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 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings: Proceedings (pp. 2306-2310). Article 8451614 IEEE. https://doi.org/10.1109/ICIP.2018.8451614

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. Article 9234624. https://doi.org/10.1109/TSP.2020.3029659

Oleksiienko, I., Nousi, P., Passalis, N., Tefas, A. & Iosifidis, A. (2024). Vpit: real-time embedded single object 3D tracking using voxel pseudo images. Neural Computing and Applications, 36(32), 20341-20354. https://doi.org/10.1007/s00521-024-10259-2

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. Article 7420734. 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

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

Amer, A. H. K. S. A., Mehndiratta, M., Sejersen, J. L. F., Pham, X. H. & Kayacan, E. (2024). Visual Tracking Nonlinear Model Predictive Control Method for Autonomous Wind Turbine Inspection. In 2023 21st International Conference on Advanced Robotics (ICAR) (pp. 431-438). IEEE. https://doi.org/10.1109/ICAR58858.2023.10406329

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

Therkildsen, O. R. & Nyholm Jørgensen, R. (2018). Vildtvenlige Og Simple Høststrategier kan Reducere Høstdrab. Danske Maelkeproducenter, (2), 38-39.

Olesen, C. R., Nielsen, L., Hald, A. B., Fris, H. J., Madsen, C. F., Nyholm Jørgensen, R., Larsen, R. & Laursen, M. S. (2015). Vildtforvaltning i forbindelse med biomassehøst i ådale: Bedre forhold for vildtet og færre påkørsler.

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

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

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

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, Article 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., Nousi, P., Passalis, N., Tefas, A. & Iosifidis, A. (2023). Variational Voxel Pseudo Image Tracking. In 2023 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 323-328). IEEE. https://doi.org/10.1109/SSCI52147.2023.10371810

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

Oleksiienko, I., Tran, D. T. & Iosifidis, A. (2023). Variational Neural Networks. Procedia Computer Science, 222, 104-113. https://doi.org/10.1016/j.procs.2023.08.148

Naveed, K., Meldgaard Madsen, L., Grombacher, D. & Larsen, J. J. (2023). Variational Mode Decomposition-Based Processing of Time-Domain Induced Polarization Data. Abstract from NSG2023 29th European Meeting of Environmental and Engineering Geophysics, Edinburgh , United Kingdom. https://doi.org/10.3997/2214-4609.202320133

Magris, M. & Iosifidis, A. (2023). Variational Inference for GARCH-family Models. In ICAIF 2023 - 4th ACM International Conference on AI in Finance (pp. 541-548). Association for Computing Machinery, Inc.. https://doi.org/10.1145/3604237.3626863

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

Laursen, M. S., Midtiby, H., Jørgensen, R. N. & Krüger, N. (2012). Validation of Modicovi - Monocot and Dicot Coverage Ration Vision based method for real time estimation of Canopy Coverage Ration between Cereal Crops and Dicotyledon Weeds.

Gislum, R. & Nyholm Jørgensen, R., (2018). Valg af biomasseindeks i pilotprojekt om efterafgrøder/biomasse, No. 2018-760-000909, 4 p., Nov 09, 2018.

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. In Proceedings of ISCA ITRW - Speech Analysis and Processing for Knowledge Discovery

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.

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

Dobbs, A. M., Ginn, D., Skovsen, S. K., Yadav, R., Jha, P., Bagavathiannan, M. V., Mirsky, S. B., Reberg-Horton, C. S. & Leon, R. G. (2023). Using structure-from-motion to estimate cover crop biomass and characterize canopy structure. Field Crops Research, 302, Article 109099. https://doi.org/10.1016/j.fcr.2023.109099

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) https://doi.org/10.1007/978-3-319-26129-4_19

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), Article 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, Article 106401. https://doi.org/10.1016/j.asoc.2020.106401

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

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AU Engineering