“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)
Communications of the ACM – Holograms on the Horizon 22.12.2021
ETRO and IMEC built-up a reputation in the domain of computer-generated holography. This technology is key to providing high-end media content for holographic display and printing. Recently, ETRO presented a novel approach combining classical raytracing approaches in computer graphics and state-of-the-art computer-generated holography techniques. In an interview in Communications of the ACM, David Blinder discusses (alongside other researchers from MIT and the universities of Stanford and Cambridge) the challenges that are currently being faced.
Links to relevant literature:
• Signal processing challenges for digital holographic video display systems, Signal Processing: Image Communication 70 (2019) 114-130. https://doi.org/10.1016/j.image.2018.09.014
• “Photorealistic computer generated holography with global illumination and path tracing,” Opt. Lett. 46, 2188-2191 (2021). https://doi.org/10.1364/OL.422159
Picture taken in ETRO visual testing lab
First VUB building (Transitorium) on brand new VUB Campus (old exercise field for the gendarmerie).
Start of the ETRO – Electronics Lab. Founder Prof. Oscar Steenhaut (two tracks: electronic circuits and systems and semiconductor technology and devices); lots of empty cupboards, two transistor testers and 1 oscilloscope.
Johan Stiens, the ETRO representative in the working group of “Digital for Climate” of the Alliance for IoT and Edge Computing Innovation, https://aioti.eu/
is co-author with a group of 20 people of a final report (+ 80 pages) on “IoT and Edge Computing Carbon Footprint Measurement Methodology”  (Release 1.1)
https://aioti.eu/wp-content/uploads/2022/11/AIOTI-Carbon-Footprint-Methodology-Report-Final-R1.1.pdf
The goals of this report are multifold:
• To help users of IoT and Edge Computing technologies and services, to understand and make informed choices on how to assess the carbon footprint of solutions and services they use, and to as well to measure how these methodologies support carbon footprint reduction of their use
• To present initiatives and standards, existing methodologies of measuring ICT carbon footprint and how they can be applied to IoT and Edge Computing
• To present selection methodology criteria and how to measure benefits of using them in reducing carbon footprint when using IoT and Edge Computing technologies and services for several industrial domains
• To propose a method of calculating the carbon avoided emissions in an industrial sector/domain, when ICT is used as an enabling technology
On June 20th 2024 at 16:00, Yangxintong Lyu will defend their PhD entitled “DEEP-LEARNING-BASED MULTI-MODAL FUSION FOR TRAFFIC IMAGE DATA PROCESSING”.
Everybody is invited to attend the presentation in room I.0.02, or digitally via this link.
In recent years, deep-learning-based technologies have significantly developed, which is driven by a large amount of data associated with task-specific labels. Among the various formats used for representing object attributes in computer vision, RGB images stand out as a ubiquitous choice. Their value extends to traffic-related applications, particularly in the realms of autonomous driving and intelligent surveillance systems. By using an autonomous driving system, a car is capable of navigating and operating with diminished human interactions, while traffic conditions can be monitored and analysed by an intelligent system. Essentially, the techniques reduce human error and improve road safety, which significantly impacts our daily life.
Although many visual-based traffic analysis tasks can indeed be effectively solved by leveraging features extracted from a sole RGB channel, certain unresolved challenges persist that introduce extra difficulties under certain situations. First of all, extracting complicated information becomes demanding, especially under erratic lighting conditions, raising a need for auxiliary clues. Secondly, obtaining large-scale accurate labels for challenging tasks remains time-consuming, costly, and arduous. The former prompts exploration into capturing and exploiting additional information such that the objects can be observed from diverse aspects; in contrast, the latter requires either an increase in the volume of available data or the capability to learn from other datasets that already possess perfect labels.
In this thesis, we tackle multi-modal data fusion and data scarcity for intelligent transportation systems. Our first contribution is a novel RGB-Thermal fusion neural network for semantic segmentation. It ensures the segmentation under limited illumination. Our second contribution is a 3D-prior-based framework for monocular vehicle 6D pose estimation. The use of 3D geometry avoids the ill-posed pose prediction from a single camera viewpoint. Thanks to the extra 3D information, our novel method can handle distant and occluded vehicles. The third contribution is a real-world, large-scale vehicle make and model dataset that contains the most popular brands operating in Europe. Moreover, we propose a two-branch deep learning vehicle make and model recognition paradigm to reduce inter-make ambiguity. The last contribution is a weakly supervised vehicle 6D pose estimation paradigm by adapting knowledge built based on a novel synthetic dataset. The dataset includes a large amount of accurate labels for vehicles. By learning from the synthetic dataset, our method allows the significant reduction of expensive real-life vehicle pose annotations.
Comprehensive experimental results reveal that the newly introduced datasets hold significant promise for deep-learning-based processing of traffic image data. Moreover, the proposed methods surpass the existing baselines in the literature. Our research not only yields high-quality scientific publications but also underscores its value across both academic and industrial domains.
The Institute of Radio-Electronics in Moscow is studying properties of III-V semiconductors with a view to more efficient components (opto-electronic modulators, detectors, switches, sensors) and research is started in the domain of mm waves, a virtually non-existent activity in Western Europe with the application of detection of hidden objects (weapons, explosives) for scanning in airports, among others.
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.