“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)
Benyameen Keelson and Pieter Boonen sucessfully finished the LifeTech.brussels MedTech accelerator with their startup projects PADFLOW en KARMA.
Some extra infomation can be found here.
- https://www.linkedin.com/posts/lifetech-brussels_medtechaccelerator-juryprize-publicprize-activity-7250177652748169216-x5-_?utm_source=share&utm_medium=member_desktop
- https://lifetech.brussels/en/medtech-accelerator/
- https://lifetech.brussels/en/innovation-in-progress/
The introductory movies for the projects:
Benyameen:
Pieter:
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A new technique helps surgeons better visualize cancer cells during operations, improving their precision in removing tumors. Existing imaging methods like MRI or CT scans often lack the detail needed to clearly distinguish cancerous tissue from healthy tissue. While fluorescence-guided imaging uses special contrast agents that emit light to highlight tumors, it still struggles to show clear borders. To solve this, researchers developed fluorescence lifetime imaging, which measures how long the contrast agent glows, giving a more accurate picture of the tumor’s edges. ETRO has created a special camera for this purpose, which is now being tested on dogs before it is used in human surgeries, with the goal of making cancer operations safer and more effective.
On April 25th 2025 at 16:00, Ayman Morsy will defend their PhD entitled “A NOVEL APPROACH TO DEPTH-SENSE IMAGING USING CORRELATION-ASSISTED DIRECT TIME-OF-FLIGHT”.
Everybody is invited to attend the presentation in room I.0.03 or online via this link.
Time-of-flight (ToF) imaging has emerged as a vital technology in machine vision and sensing, expanding into applications such as augmented and virtual reality, gaming, robotics, autonomous driving, autofocus, and facial recognition on smartphones and laptops. ToF technology determines the distance to an object within the detection range by emitting a light source and measuring the time it takes to return. This round-trip time determines the object’s distance, with different sensing technologies employing distinct methods to determine this time.
For ToF applications, developing sensors with high image resolution, low power consumption, and the ability to function reliably in high ambient light conditions is desirable. This dissertation presents the development of a novel single-photon avalanche diode (SPAD)-based pixel called Correlation-Assisted Direct Time-of-Flight (CA-dToF), designed for in-pixel ambient light suppression and characterized by low power consumption and a scalable pixel structure. The CA-dToF pixel uses a laser pulse correlated with two orthogonal sinusoidal signals as input to two switched capacitor channels, which average out detected ambient light while accumulating the laser pulse round-trip time.
To gain insights into CA-dToF pixel operation, both Python simulation and analytical modeling were developed. Two generations of the CA-dToF pixel were developed and characterized, with the second-generation pixel achieving the first operational performance under high ambient light conditions. The two-generation CA-dToF pixel was tested under various lighting conditions and pixel design variations. Additionally, noise sources within the pixel implementation were analyzed, and potential solutions were proposed.
On May 27th 2024 at 16:00, Yifei Da will defend their PhD entitled “DATA-DRIVEN CAUSAL MODELLING FOR DE-BIASING SENTIMENT ANALYSIS MODELS AND MULTIVARIATE STOCK PRICE MOVEMENT PREDICTION”.
Everybody is invited to attend the presentation in room I.0.01, or digitally via this link.
In this thesis, we address the stock price movement prediction problem by investigating the interdependencies between sentiments from financial news and international stock markets in stock forecasting. To provide reliable sentiment analysis results, especially to reduce bias, we studied sentiment analysis methods and detected, evaluated, and mitigated bias that was picked up on and amplified by large pre-trained models. To retrieve intra- and inter-market interdependencies, we adopt the Transfer Entropy theory to detect and incorporate the information flow between financial news sentiment and the dynamics of the stock markets. We contribute to these two sub-tasks by (i) proposing a new method for debiasing sentiment analysis models that leverages the causal mediation analysis to identify the parts of the model primarily responsible for the bias and apply targeted counterfactual training for model debiasing. Furthermore, (ii) a causal-enhanced multi-modality model for multivariate stock price movement prediction is proposed based on establishing an accurate information flow propagation between stocks and sentiments. To repeatedly validate the feasibility, the Dow Jones Industrial Average indexes of 13 countries and daily financial news data from the New York Times are used in stock Price and Return forecasting.
ETRO was highly visible and omni-present at the HealthTech Brussels event hosted by FARI, showcasing cutting-edge AI expertise in health. We hope the networking opportunities helped create valuable new connections with the entrepreneurs and clinicians who attended.
