On June 17th 2024 at 10:00, Ruben De Smet will defend their PhD entitled “RAPID PROTOYPING AND DEPLOYMENT OF PRIVACY-ENHANCING TECHNOLOGIES”.

Everybody is invited to attend the presentation in room D.2.01, or digitally via this link.

Since its inception, the internet has quickly become a public service utility. The combination of its commercial exploitation, and the rather intimate nature of how humans actively use the internet, gives rise to some paradoxical situations. As a citizen of Belgium, I would probably not expect to give my name and phone number to a company in the United States to talk to my brother, 50 km up north. However, for over two billion people, this is their rather paradoxical reality: the company Meta, owning WhatsApp, collects and stores these data for their users. This cherry-picked scenario stands example for a wider trend in the industry.

Cryptographers have worked on several privacy-enhancing technologies (PETs). These PETs aim to minimize the amount of personal data to fulfil a service for users. Although these technologies exist on paper, several practical issues arise. These practicalities are the subject of this thesis.

One practical issue is the performance. PETs that run on end-user devices should both be fast and require little bandwidth. We investigate how implementation details may lead to significant speedups or bandwidth savings. Specifically, we devise a zero-knowledge proof (ZKP) tailored to electronic road pricing (ERP). ERP is a privacy-sensitive topic, and our ERP system achieves some notable performance improvements over preexisting proposals.

A second practical issue is the challenging nature of implementing PETs. We present “Circuitree” and “Whisperfish”, to study how to bring PETs to an actual application. Circuitree is a high-level framework to tailor ZKPs to specific scenarios, using a bespoke logic programming language. The language is designed such that the resulting ZKP is highly efficient.

Whisperfish is effectively a reimplementation of the Signal instant messaging client, and allows us to present in detail how Signal deploys their PETs to users. All ideas put forward in this thesis were evaluated by means of their implementation in the Rust programming language.

The introductory part will last approx. 45 minutes. It consists of a brief overview of weather and climate studies and will be followed by a (very) brief introduction of machine learning. The two subjects will be tied together by looking into the potential of AI techniques in the field.Lastly, two specific examples will be presented in more detail: (1) Attempts to replace the radiation scheme with a deep learning model to reduce computational cost and (2) Using U-nets and GAN's for super resolving (downscaling) over Germany.The talk will be held on Thursday 13/10/2022 at 09:30 am in the Marconi meeting room (2nd floor of Pleinlaan 9). It is also possible to follow the talk online using this link.We hope to see you there (or online)!

On 30 October 2026 at 16:00, Silvia Zaccardi will defend their PhD entitled “Beyond the lab: Towards portable clinical motion analysis with observer-worn head-mounted displays”.

Everybody is invited to attend the presentation in room LIC.0.04 or online via this link.

Human movement analysis provides valuable information for assessing patients and monitoring rehabilitation. However, accurate measurements typically require expensive equipment in specialised laboratories, limiting their use in everyday clinical practice.

This thesis explores how Artificial Intelligence (AI) and Mixed Reality (MR) headsets could make movement analysis more affordable, portable, and accessible. The long-term vision is a system in which a physiotherapist wears an MR headset while a patient walks or performs exercises in front of them. Cameras built into the headset would track the patient’s movements, while relevant information, such as joint angles and walking characteristics, would be displayed directly in the physiotherapist’s field of view, supporting clinical assessment in real time.

Towards this vision, the research proposes two approaches to capturing human movement using the headset’s built-in cameras and sensors. The first uses AI to estimate body movement directly from video, without requiring physical markers, while the second uses small reflective markers attached to the patient’s body to improve tracking accuracy. The thesis also explores AI algorithms to estimate the forces between the feet and the ground from movement data alone, potentially reducing the need for specialised force-measuring plates embedded in the laboratory floor.

The findings demonstrate the potential of these technologies while highlighting the trade-offs between measurement accuracy, ease of use, and real-time performance. Overall, this thesis contributes towards making objective movement analysis more accessible beyond specialised laboratories.

On November 15th 2024 at 10:00, Eden Teshome Hunde will defend their PhD entitled “CROSS-LAYER DESIGN, IMPLEMENTATION AND EVALUATION OF IPV6 MULTICAST FOR RADIO DUTY CYCLED WIRELESS SENSOR AND ACTUATOR NETWORKS”.

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

In this work, we study Bidirectional Multicast RPL Forwarding (BMRF) as this protocol relies on forwarding tables put in place by the well-known Routing Protocol for Low Power and Lossy Networks (RPL) and allows to combine the best ideas of existing multicast protocols. Through RPL, a routing tree towards the sink is installed for multihop routing from node to sink, and the nodes’ forwarding tables will also contain entries for reaching destinations in downward direction.

For downward forwarding IPv6 multicast packets, two methods exist. One is via link layer (LL), broadcasting a frame containing the IPv6 multicast packet. The other is to send several LL unicast frames containing that packet. BMRF allows a node to choose between these two methods. The best option will depend on the presence of a radio duty cycling (RDC) protocol. RDC is part of the medium access control (MAC) layer and puts the radio to sleep when no communication is needed. We investigate the influence of MAC/RDC protocols on BMRF’s performance.

We evaluate the performance of BMRF on non-synchronized WSANs that use Carrier Sense Multiple Access (CSMA) as MAC and ContikiMAC as RDC. We demonstrate that LL unicast outperforms LL broadcast in terms of packet delivery ratio (PDR), delay, and energy consumption in many settings.

We investigate the performance of BMRF on WSANs with synchronous MAC and RDC based on Time Slotted Channel hopping (TSCH). This is more challenging, as TSCH needs a schedule to tell which action must happen in each timeslot. The actions can be to send or to listen on a given channel or to be idle. Idleness allows the radio to switch OFF, providing RDC. The schedule is not part of the standard and must be proposed by the system designer. An elegant autonomous scheduling method called Orchestra is available to accommodate traffic in a RPL tree. We extend Orchestra with a novel scheduling rule for supporting LL downwards forwarding through LL broadcast. Comparing LL unicast with LL broadcast forwarding teaches us that LL unicast outperforms LL broadcast in terms of packet delivery ratio (PDR), but the latter can be beneficial to certain applications, especially those sensitive to delay.

Before conducting the two previous evaluation studies, we investigate the performance of simple convergecast traffic while considering ContikiMAC and TSCH with Orchestra under RPL on the real dual Zolertia Firefly Motes (one is observed and other one is observing mote). This study served two purposes; it reminds the reader of the characteristics of those protocols and allowed to fine-tune the dual motes.

We also contributed by adapting the Orchestra to bursty convergecast traffic. Simulation results demonstrate that the new scheduler slightly improves PDR and reduces delay compared to state-of-the-art solutions.

After two years of dedicated research and development under the leadership of ETRO-VUB, a breakthrough has been achieved within the INTOWALL project: a revolutionary radar technology for building inspection was developed, called the transient radar method (TRM). The initiative aimed to reduce the CO2 emissions of buildings and increase their energy efficiency.

The new technology enables the measurement of the density of glass wool in cavity walls with unprecedented precision, without the need for invasive methods. “This advancement not only promises to improve the accuracy of insulation assessments but also contributes to the ambition to achieve a CO2-neutral status by 2050,” says Professor Johan Stiens of ETRO.

Looking towards the future, the project team is focused on further refining the technology to map a wide range of insulation materials and building elements. This prospect of expansion and application on a larger scale highlights the endless possibilities. As part of the FTI Brussels Festival, the milestone of the INTOWALL project will be celebrated. A unique demonstration was held on March 18, 2024.

Additionally, the project team invites potential partners to contribute to and participate in this groundbreaking endeavour. Through collaboration, we can transform the construction sector into a more sustainable and efficient future.

For more information on InToWall press articles: https://press.vub.ac.be/wereldprimeur-in-radartechnologie and https://trends.knack.be/kanaal-z/z-nieuws/bekijk-radar-van-vub-ziet-isolatie-dwars-door-muren-heen/

On April 2nd 2026 at 16:00, Sebastian Amador Sanchez will defend their PhD entitled “Advancing Landmark Localization through Deep Segmentation for Reliable Malalignment Assessment in Lower Limb Radiographs”.

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

Knee osteoarthritis affects millions worldwide and is frequently associated with lower limb malalignment. Clinical assessment of malalignment relies on manual landmark identification in X-ray images, a time-consuming process prone to interobserver variability. While most automated approaches use regression-based deep learning, this thesis investigates image segmentation with circular masks centered on landmark locations as an alternative strategy.

First, we introduce a segmentation-guided coordinate regression framework that integrates a segmentation network with a coordinate regression branch, trained end-to-end. This hybrid approach improves localization accuracy over standard regression and increases robustness compared to standalone segmentation, thereby mitigating false positives and missed detections.

Second, we optimize segmentation-based methods by evaluating architectures, post-processing strategies, and mask sizes. A fully convolutional model trained with radius-15 masks, combined with adaptive threshold-based centroid extraction, outperformed conventional landmark localization approaches, with improved performance in knee phenotype classification.

Third, we propose a Siamese network trained with a contrastive loss for quality control that detects inaccurate predictions by comparing image patches to reference embeddings. The method reliably identifies errors exceeding 2.0 mm and estimates their magnitude, outperforming baseline methods.

Overall, this thesis advances the field of landmark localization and demonstrates its clinical relevance for the automated assessment of lower limb malalignment. Beyond landmark localization accuracy, our contributions address robustness and failure identification, two aspects that are often overlooked yet vital for future clinical deployment.