“Signal Processing in the AI era” was the tagline of this year’s IEEE International Conference on Acoustics, Speech and Signal Processing, taking place in Rhodes, Greece.
In this context, Brent de Weerdt, Xiangyu Yang, Boris Joukovsky, Alex Stergiou and Nikos Deligiannis presented ETRO’s research during poster sessions and oral presentations, with novel ways to process and understand graph, video, and audio data. Nikos Deligiannis chaired a session on Graph Deep Learning, attended the IEEE T-IP Editorial Board Meeting, and had the opportunity to meet with collaborators from the VUB-Duke-Ugent-UCL joint lab.
Featured articles:
- De Weerdt, B. et al., “Designing Transformer Networks for Sparse Recovery of Sequential Data Using Deep Unfolding” (https://ieeexplore.ieee.org/abstract/document/10094712)
- Yang X. et al., “Relevance Propagation through Deep Conditional Random Fields” (https://ieeexplore.ieee.org/abstract/document/10095075)
- Stergiou A. et al., “Play it back: Iterative attention for audio recognition” (https://ieeexplore.ieee.org/abstract/document/10096532, developed at the University of Bristol)
A research team of ETRO was selected (under supervision of Prof. Johan Stiens and Dr. Bruno Da Silva) to participate to the I-LOVE-SCIENCE FESTIVAL (15-16-17/10/2021) in BRUSSELS with demos of wearable devices.
During the festival, the visitor will be introduced to various existing portable/wearable medical measuring instruments and their operating principles by Angel, Joan, Salar, Vlad, Bruno and Johan. The visitor will be able to experiment with various technologies to detect different physiological signals of his/her body under different conditions of activity. The measurement systems are specially designed for educational purposes, such that the user will also be able to change settings themselves and check their influence (a little engineering experience).
In addition to the technical-medical aspects, the social relevance will also be explained: how this measurement technology can contribute to preventive medicine, extremely important for social cost reduction of the health care costs.
Bachelor students in their last year of study can apply for the program with the condition to successfully finish their Bachelor studies before the start of the academic year.
On March 31 2021 at 16.00 Panagiotis Tsinganos will defend his PhD entitled “Multi-channel EMG pattern classification based on deep learning”.
Everybody is invited to attend the presentation online via https://upatras-gr.zoom.us/j/98941099749?pwd=ZmdQZkxRYllIaVRDKzJrVHM2L2krQT09
In recent years, a huge body of data generated by various applications in domains like social networks and healthcare have paved the way for the development of high performance models. Deep learning has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks. Combined with advancements in electromyography it has given rise to new hand gesture recognition applications, such as human computer interfaces, sign language recognition, robotics control and rehabilitation games.
The purpose of this thesis is to develop novel methods for electromyography signal analysis based on deep learning for the problem of hand gesture recognition. Specifically, we focus on methods for data preparation and developing accurate models even when few data are available. Electromyography signals are in general one-dimensional time-series with a rich frequency content. Various feature sets have been proposed in literature however due to the stochastic nature of the signals the performance of the developed models depends on the combination of the features and the classifier. On the other hand, the end-to-end training scheme of deep learning models reduces the effort needed for finding the best features and classification model, yet a suitable preprocessing of the signals is still required. Another problem is that variations in gesture duration, sensor placement and muscle physiology require continuous adaptation of the trained models using new recorded data.
The implementation is based on surface electromyography sensors, which comprise the input to the end-to-end deep learning pipelines that process and classify the electromyography signals. Preprocessing and data preparation techniques for electromyograms are examined, while data augmentation and transfer learning approaches allow developing personalised models even when few data are available. Besides their successful application in other domains, the use of deep learning models allows the development of systems that can easily generalise to new users. The use of electromyography sensors is important because the developed system can detect whether any unwanted compensatory movements are performed, which under typical vision-based interfaces is impossible.
The advancements proposed in this thesis have been evaluated with publicly available data repositories. However, considering that the models are trained in an end-to-end fashion they can be easily adapted to different setups.
His speach is entitled “Klimaatverandering in een historisch, mondiaal en antropogeen perspectief“, or “climate change in a historical, mondial and antropogene perspective”. In this short introduction, we look at the current climate change at a larger time scale and across the different regions of the world. We highlight some of the relationships with the world population increase and the local and mondial economic activity, focusing on energy consumption. We finish the talk with a vision on the future in the domains of energy sources and transitions in automotive
More info at:
Anass Hamdi got awarded an PhD fellowship strategic basic research for his research “AI-driven radiogenomic analysis for spatial glioblastoma subtyping” under supervision of Catharina Olsen, Jef Vandemeulebroucke, Johnny Duerinck and Wim Vranken.
De Vrije Universiteit Brussel wil met een supercomputer rampen zoals de waterbom in WalloniĂ« van 4 jaar geleden, beter kunnen voorspellen. De nieuwe supercomputer kan met AI de informatie die radars en satellieten leveren sneller verwerken en zo sneller updates geven over de weersomstandigheden.Â
https://www.vrt.be/vrtnws/nl/2025/11/30/supercomputer-rampen-waterbom