Johan Stiens gave a lecture @ the atheneum Geel in the framework of PACT

“Technology, Humanity, and the Climate Imperative: Engineering a Sustainable Future”

 Climate change is no longer a distant threat—it is a defining reality shaping our planet, economies, and societies. This keynote invites participants to take a bird’s-eye view of the interconnected forces driving this transformation and to explore how technology, data, and global citizenship can converge to create a sustainable future. We begin with critical observations on climate change and its cascading impact on population dynamics and economic resilience. From there, we examine the evolving energy mix, where renewable sources are not just alternatives but imperatives, demanding innovation in materials, transistors, and processors that power both ICT and solar technologies.

As we enter the data era, ICT systems and smart IoT solutions are unlocking unprecedented sectorial benefits—from MedTech and HealthTech to agri-food and construction—while enabling biodiversity and sustainability at scale. These advances are not merely technical; they represent a societal shift toward intelligent, resource-efficient ecosystems. Drawing on decades of experience in sensor technology development and active engagement with global organizations, this talk will challenge academia, industry, and policymakers to embrace technology uptake as a catalyst for systemic change. Ultimately, the question is not only how we innovate, but how we become true global citizens—responsible stewards of the planet we share, and architects of a future where digital intelligence and clean energy work hand in hand to safeguard life on Earth.

New video on the INTOWALL project .

Tripat Kaur followed the Masters in Applied Computer Science in 2020. His interest in Computer Science goes back to his school days. Not only the theory classes were very interesting, he also enjoyed the practical assignments to the fullest. His first-ever programming language C++. was a thrill. He made his first program on the blue screen of Turbo to get a ‘Hello Tripat’ output on the black screen. Ever since programming has been his passion.

The MACS program started teaching from a very basic level. Even if one had no computer science background, it was not a problem because everything is taught from scratch, but the study load in the first year was very high. Tripat enjoyed Advanced Programming Concepts the most by far. It helped him improve his programming skills a lot. Tripat studied during the corona year and his first year was through remote teaching. It was very different from what he had imagined. Even though being in different countries, his fellow students made sure that he did not have to worry about the time differences. They always adjusted with him making it fun to work on team projects. Tripat is now a more confident person as his horizons have widened. Not only technically but even his soft skills have improved thanks to this program. Tripat says: Never be afraid to dream. Word hard and believe in yourself, things will fall into place. His ideal future is to be completely independent and settled not only in terms of finances but in terms of happiness and confidence. Also, being able to support his family the way they have till now.

For scholarship opportunities you can check our website: http://www.vub.ac.be/en/study/applied-sciences-and-engineering-applied-computer-science#practical-info

All students who apply for this program are eligible to be awarded with a VUB Scholarship. The scholarships will be awarded on a competitive basis among all accepted applicants.

On April 21 2023 at 14.00, Quentin Bolsée will defend his PhD entitled “CALIBRATION AND PREPROCESSING OF LIGHT FIELD AND MULTIVIEW DEPTH SYSTEMS”.

Everybody is invited to attend the presentation in room I.2.01 or via this link.

Recently, there has been an increasing demand for high quality 3D content, yet there remains a gap with what real-time depth sensors are capable of. Active sensors such as Time-of-Flight cameras still produce excessively noisy data, while passive technology (photogrammetry, Light Fields) coupled with depth estimation is nowhere near real-time and still presents missing information for challenging scenes. Deep learning has shown promising results in both areas, although the actual properties of physical sensors are almost always neglected.

In this work, properties of multiview depth camera setups are thoroughly examined towards producing a high quality geometry acquisition system. First, a novel calibration step is proposed for a global optimization of the multiple camera parameters using a custom 3D object covered with charuco markers. The noise models are then discussed, and a residual learning convolutional neural network is shown to greatly reduce it. When merging the results from several cameras, a novel refinement step is applied with a pointnet-like neural network constrained to shift 3D points along their viewing ray. This provides a correction on the depth map that preserves the pixel structure while harnessing properties of natural 3D surfaces and observations from other cameras. Combined with the preprocessing by the convolutional neural network and flying pixel removal, this approach is shown to outperform state-of-the-art noise removal methods in both depth map and 3D domain.

In the second part of the thesis, properties of light field systems are discussed, and a new geometrical model is proposed when calibrating microlens arrays in modern Light Field cameras. Unlike previous works, lens distortion parameters are added to the description of the microlens, leading to a non-constant baseline in the virtual camera array. The calibrated model is shown to outperform the state of the art when applied to stereo matching depth estimation. The topic of depth estimation is further studied by showcasing a new 3D convolution-based neural network successfully applied on synthetic light field datasets. The main advantage is a significant reduction in the number of training parameters by treating the camera index as a third dimension, exploiting its isotropy. Finally, a motorized 2-DOF device for spherical light field acquisition is presented and calibrated with a 3D object similar to the one previously described for multiview depth systems. Global optimization of the sphere and camera parameters leads to a sub-pixel accuracy and high-quality depth estimation. Those results are confirmed by comparing a captured image with its reconstruction from neighboring virtual cameras using depth-based view synthesis.

Students who think they have had the same course before, have to fill out the form for exemption, (https://student.vub.be/en/ir#regulations-and-forms) and together with the transcripts send everything to the faculty secretary office.