On February 1 2021 at 16.00 Tien Do Huu will defend his PhD entitled “A generic source coding. Methodology and architecture of dynamic holograms”.

Everybody is invited to attend the presentation online via https://teams.microsoft.com/l/meetup-join/19%3ameeting_N2EwMTQ2ZmMtMjA2Ny00MGQwLTk2ZDctODNmMzE3NGEyNDMz%40thread.v2/0?context=%7b%22Tid%22%3a%22695b7ca8-2da8-4545-a2da-42d03784e585%22%2c%22Oid%22%3a%2237757df7-4050-47fe-8384-aded7e9e79c3%22%7d

We address the two challenges of big heterogeneous data originated from social media and smart cities, namely data quality enhancement and data exploitation. In the first challenge, we consider several subproblems commonly found in social media data and smart city data, including user location prediction, traffic data denoising, and hyperlocal air quality prediction. For the second challenge, we aim to gain insights from data, namely we focus on detecting fake news using social media data. As there exist correlations across datapoints in social media and smart city data, we propose exploiting these correlations using graph-based deep learning techniques to address the aforementioned challenges.

Our contributions are associated with the concerned subproblems. In user location prediction, we propose a novel deep multiview model combining multiple aspects of social media data. One of the inputs of the multiview model is node representation, which is learned using a graph-deep-learning-based technique. In order to denoise traffic data, we design a special graph auto-encoder with a Kron-reduction-based pooling scheme. We devise a graph variational auto-encoder in dealing with the air quality prediction problem. For fake news detection, we propose using a graph convolutional neural network, which captures the relation between articles shared by suspicious publishers. Having experienced graph neural networks on different applications, our last contribution focuses on a more fundamental problem of regularizing graph neural networks by introducing a novel regularization technique based on dropping nodes. We conduct comprehensive experiments on benchmark datasets to verify the effectiveness of the proposed methods. Finally yet importantly, although some of our methods are designed for specific problems (e.g., air pollution prediction), our formulations are general, leading to the possibility of using these methods for different applications (e.g., recommender systems) in related domains.

Sofia Granda attended the Master in Biomedical Engineering in 2020-2021. She chose the program because she really liked mathematics, physics, and biology at high school and liked to be able to find practical solutions to problems. Sofia described the program in the following three words: Empathy, Logic and Medicine. Strengths of the program were the flexibility in the second year choosing the electives from a very wide offer. It included many practical sessions and visits to the hospital. But sometimes is was difficult to understand the global picture and the purpose of some contents of the program. There were some overlaps. Her favorite course was Health Information and Decision support systems. The collaboration with the other students from different cultures lead sometimes towards cumbersome communication but in the end, it was enriching. Sofia’s golden tip for future students is: Be true to yourself and don’t be afraid of following your goals, even when you get demotivated due to bad scores or difficulties with learning, especially with courses you don’t like but that are mandatory.

Sofia would like to end up applying her knowledge improving people’s lives or investigating in a job that fulfills her and that she is proud of.

Happy kids visited the ETRO Build your climate-proof LEGO city boot at  CurieuCity and it was also broadcasted on Bruzz tv this weekend.

https://curieucity.brussels/nl/build-your-climate-resistant-city-of-the-future/

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.

4D CT scanners add the dimension of time to three-dimensional images and visualise the movement of the heart in detail. The imec.icon project DIASTOLE, involving VUB, UZ Brussel and imec, is paving the way to safely implement 4D scans in heart surgery.
Researchers from the radiology department of VUB-UZ Brussel developed a model to calculate the radiation dose of 4D scans on the skin, and immediately applied it to draw up a safe protocol. For a usable 4D scan, on the one hand the quality has to be sufficient, on the other hand you want to avoid the radiation dose being too high at certain places on the body. Unlike classic CT scans, a 4D scan repeatedly irradiates the same region of the body, so we need to specifically monitor the dose to the skin.

https://press.vub.ac.be/cardiology-prepared-for-the-fourth-dimension

The program is organised to accommodate your scientific background and future-oriented academic interests – developing the necessary Computer Science and Data Science skills by complementing your primary field of expertise. Above all that, we offer a wide variety of highly specialised elective courses;

The Master of Applied Computer Science provides a broad education in data science and engineering with focus on generic smart systems design, complemented with elective minors in digital health, smart cities, environmental informatics, and business intelligence. The accumulated knowledge will give rise to an ICT engineer, capable to design systems of systems and apply analytics on the heterogeneous data obtained by such systems.