“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)

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.

Loris Giordano got the best student paper award at the AMAI workshop of MICCAI 2025 for the paper “A modular deep-learning pipeline for automated aorta characterization on CT”, co-authored by Loris Giordano, Jakub Ceranka, Selene De Sutter, Kaoru Tanaka, Gert Van Gompel, Tom Lenaerts, and Jef Vandemeulebroucke.

Anass Hamdi got awarded an PhD fellowship strategic basic research for his research “AI-driven radiogenomic analysis for spatial glioblastoma subtyping” under supervision of Catharina Olsen, Jef Vandemeulebroucke, Johnny Duerinck and Wim Vranken.

Sarah Al Omari got awarded a PhD Fellowship fundamental research for her research “Exploring Neuromuscular Fatigue in Stroke Survivors: Central-Peripheral Interplay and the Potential of Transcranial Alternating Current Stimulation (tACS)” under supervision of Eva Swinnen, David Beckwée, Mahyar Firouzi and Bart Jansen.

Manuel Montoya received the best oral presentation award at the International Conference on Applied Physics & Imaging (ICAPI) 2025 in Tartu, Estonia, for the work “Efficient simulations of partially coherent light using the Generalized Van Cittert – Zernike Schell Propagator”, co-authored by Manuel Montoya, Maria J. Lopera Acosta, Yunfeng Nie, and David Blinder.

On October 8th 2025 at 16:00, Xinxin Dai will defend their PhD entitled “LEARNING BASED RECONSTRUCTION AND MEASUREMENT OF 3D HANDS USING A SINGLE DEPTH CAMERA”.

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

Accurate 3D reconstruction and measurement extraction of the human hand are critical for a wide range of hand-centric applications, such as the design of immobilization devices, prosthetic limb fabrication, and osteoarthritis evaluation. However, the recovery of high-fidelity hand geometry remains challenging due to the inherently incomplete and occluded nature of point clouds acquired from commodity depth sensors, which are limited by viewpoint constraints and self-occlusion. Furthermore, traditional manual measurement methods, which require static hand postures and the expertise of trained anthropometrists, are inadequate for capturing measurements under realistic, task-specific hand motions, limiting their applicability in dynamic or non-standard scenarios.

To address these limitations, this thesis introduces deep learning-based methodologies aimed at addressing key challenges in the reconstruction and measurement extraction of 3D hand shapes. Specifically, the main research challenges include: (i) What is the optimal hand posture for precise and reliable measurement? (ii) How to fast and precisely reconstruct a complete hand shape from multi-view partial point clouds under different postures? and (iii) How can we simultaneously complete partial point clouds and reconstruct their surfaces while preserving the raw data? (iv) How to achieve human identification by the shape and posture of hands? The first challenge derives from the complexity of the human hand, which consists of 34 muscles and 27 bones. This intricate structure enables a wide range of postural variations, often resulting in significant geometric deformations that introduce considerable biases in measurement accuracy. Second, depth cameras inherently capture only partial point clouds due to limited viewpoints and self-occlusions, resulting in incomplete representations that restrict the accurate reconstruction of full hand geometry. Third, the lack of high-resolution surface details in a single partial point cloud makes it challenging to simultaneously achieve both point cloud completion and high-fidelity surface reconstruction. Lastly, while previous studies on human identification have primarily focused on recording the velocities of pressing and releasing different keys, these approaches lack integration with vision-based hand motion analysis.

To overcome the aforementioned challenges, this thesis introduces four deep learning-based models. The first model is  Measure4DHand, designed for automatic extraction of dynamic hand measurements from partial hand point cloud sequences. By analyzing the variation in measurement values induced by skin deformation across different hand postures, this model facilitates the identification of optimal hand postures for accurate and consistent measurements. The second model is PatientHandNet, which focuses on reconstructing a high-fidelity 3D hand shape in a canonical open-palm pose using four depth images captured from different viewpoints by a single commodity depth sensor To facilitate the proposed model, a large-scale multi-view synthetic dataset with a wide variety of hand shapes and hand poses and corresponding ground truth hand shapes in a canonical open palm pose is proposed and a novel real-world dataset by capturing 18 subjects (13 males and 5 females) via a structure sensor Mark I employed in an iPad and hired a professional anthropometrist to obtain corresponding ground-truth hand biometric measurements. The third contribution proposed TailoredTemplateFit, which is, to the best of our knowledge, the first deep learning-based method in the literature is proposed to simultaneously address point cloud completion and surface reconstruction while preserving the raw data of the input. This model is trained and validated on two large-scale datasets: a large-scale 50K head dataset and 300K hand dataset with a wide variety of shapes and poses and corresponding ground truth shapes. Lastly, we present KD-Net, which explores a novel visual modality of keystroke dynamics for human identification from RGB-D image sequences. To support this research, a novel dataset dubbed KD-MultiModal is created, comprising 243.2 K frames of RGB images and depth images.

Our proposed methods consistently outperform the reference methods from the literature, as demonstrated through comprehensive experimentation. The research works have been published in various reputable journals and conferences, highlighting their impact in both academic and industrial contexts.

On October 6th 2025 at 16:00, Ran Zhao will defend their PhD entitled “DEEP LEARNING-BASED HUMAN POSTURE NORMALIZATION AND AUTOMATIC ANTHROPOMETRIC MEASUREMENT”.

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

Accurate and user-friendly anthropometric measurement remains a major challenge in computer vision, as existing approaches typically require controlled scanning conditions, standard postures, or unclothed bodies. These constraints limit their usability in practical scenarios.

This thesis proposes a sequence of deep learning-based solutions to overcome these limitations. We first introduce OrienNormNet, an iterative network for robust orientation normalization, ensuring that scans are consistently aligned without manual preprocessing. Building on this, PoseNormNet is presented as the first posture normalization framework that transforms arbitrarily posed scans into a canonical T-pose while preserving identity details, removing the need for skeleton rigging. Next, W2H-Net demonstrates the feasibility of directly estimating the waist-to-hip ratio from partial dressed scans, showing that reliable indicators can be derived even from incomplete data. Finally, MeasureXpert provides a breakthrough toward real-world usability: it enables automatic extraction of anthropometric measurements from only two unregistered, partial, and clothed scans acquired in arbitrary poses.

To support these developments, the BWM dataset was synthesized for training, validation, and evaluation. Comprehensive experiments on both synthetic and real-world data confirm the effectiveness and robustness of the proposed methods. Collectively, the contributions progressively address key challenges related to cost, posture, and clothing, moving the field closer to practical, flexible, and accessible body measurement solutions.

The algorithms presented in this thesis have been disseminated through prestigious journals and conferences, demonstrating a modest yet meaningful impact on both academic research and industrial applications.

Non-EER staff needs a single permit, this request can take up to several months. The VUB requests this but the employee needs to deliver a large list of docs.

Karin needs to know if you want to hire someone new, or if the status of your researcher changes from PhD to post doc or researcher long in advance (3 M) to make sure all the steps are taken.

Karin cannot launch the procedure without a position and cannot create a position without budget (PKC) for at least 3 M

M&O – Wie vraagt de Single permit aan?

M&O – Wat gebeurt er als mijn statuut wijzigt? (Single Permit)

Redona Brahimetaj en Elena Botti hebben de best paper award gewonnen op het IWOAR 2025 congress met hun paper “Topological Versus Spatiotemporal Gait Parameters for Fall Risk Detection with IMU Sensors” (full author list: Redona Brahimetaj, Elena Botti, Ivan Bautmans, Eva Swinnen, Bart Jansen)

On September 22nd 2025 at 16:00, Thibaut Vandervelden will defend their PhD entitled “RUST-BASED IOT NETWORKS: A NETWORK PROTOCOL AND SECURITY PERSPECTIVE”.

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

The Internet of Things (IoT) continues to transform our interconnected world. Its rapid growth raises significant concerns about privacy and security. In recent years, numerous IoT botnets have exploited vulnerabilities in embedded devices to launch large-scale Distributed Denial of Service attacks. These attacks have caused significant disruption to internet services worldwide.

Many of these security vulnerabilities come from the use of memory-unsafe programming languages, such as C and C++. Programming languages with built-in safety features can mitigate these vulnerabilities. Since its first stable release in 2015, Rust has emerged as a popular choice for system programming, gaining popularity due to its unique combination of memory safety guarantees while keeping its performance comparable to the one obtained with traditional programming languages. Developers have successfully deployed Rust across diverse domains, including Operating Systems (OSs), web services, and embedded devices.

For IoT devices, two software components are particularly important: the OS and the network stack enabling communication. We provide an evaluation of OSs and frameworks for embedded devices available in Rust and examine their suitability for various IoT applications. We also investigate the feasibility and advantages of implementing a complete network stack in Rust for resource-constrained embedded devices. We built on the smoltcp library and extended it with a Rust implementation of IPv6 over Low-power Wireless Personal Area Networks (6LoWPAN). We studied and implemented the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). We developed and applied an evaluation methodology to check compliance with the standard and to verify interoperability with existing implementations. Doing so, we solved several issues of the current RPL implementation in ContikiNG. Our research also evaluates the effectiveness of 6LoWPAN generic header compression for use in RPL networks. The results demonstrate that this compression format significantly reduces energy consumption for IEEE 802.15.4-based networks that offer low data rates. For higher data rates, the benefit of this compression format diminishes.

Alongside contributing to networking components in Rust, we also explored security primitives. We evaluated the performance of different Keccak sponge constructions, cryptographic primitives that can serve as building blocks for various security protocols. Our findings reveal that Keccak sponge constructions with lower capacity parameters achieve significantly better efficiency on 32-bit microcontrollers commonly used in IoT devices. Building on these insights, we design and implement two novel symmetric-key-based authentication protocols. One tailored for wireless sensor networks and the other for fog based networks. We demonstrate that our protocol provides robust protection against known attacks while maintaining low computational and communication overhead.

Researchers at ETRO VUB have developed the 𝗧𝗿𝗮𝗻𝘀𝗶𝗲𝗻𝘁 𝗥𝗮𝗱𝗮𝗿 𝗠𝗲𝘁𝗵𝗼𝗱 (𝗧𝗥𝗠), a non-invasive tool to visualize what’s hidden inside our buildings. TRM can:

✅ Offer a clear picture of insulation in walls
✅ Identify reinforcement types inside concrete pillars
✅ Determine moisture levels in walls
✅ Conduct year-round thermal assessments
✅ Monitor real-time carbon uptake in building materials

The team is now seeking partners to help bring this innovation to the market and transition the construction industry toward a more sustainable future.

💡 Interested in a partnership?
Join the pitch session by Prof. Johan Stiens at the Klimaat Parlement 𝗶𝗻 𝗟𝗶𝗺𝗯𝘂𝗿𝗴 𝗼𝗻 𝟭𝟵 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿 𝟮𝟬𝟮𝟱 to learn more.
You can also contact him directly at jstiens@etrovub.be.

👉 Read all the details here: https://lnkd.in/exmkJ7CU

Ali Pourkazemi, Ashkan Zarghami Iliya Hakani

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.

“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)

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.

Loris Giordano got the best student paper award at the AMAI workshop of MICCAI 2025 for the paper “A modular deep-learning pipeline for automated aorta characterization on CT”, co-authored by Loris Giordano, Jakub Ceranka, Selene De Sutter, Kaoru Tanaka, Gert Van Gompel, Tom Lenaerts, and Jef Vandemeulebroucke.

Anass Hamdi got awarded an PhD fellowship strategic basic research for his research “AI-driven radiogenomic analysis for spatial glioblastoma subtyping” under supervision of Catharina Olsen, Jef Vandemeulebroucke, Johnny Duerinck and Wim Vranken.

Sarah Al Omari got awarded a PhD Fellowship fundamental research for her research “Exploring Neuromuscular Fatigue in Stroke Survivors: Central-Peripheral Interplay and the Potential of Transcranial Alternating Current Stimulation (tACS)” under supervision of Eva Swinnen, David Beckwée, Mahyar Firouzi and Bart Jansen.

Manuel Montoya received the best oral presentation award at the International Conference on Applied Physics & Imaging (ICAPI) 2025 in Tartu, Estonia, for the work “Efficient simulations of partially coherent light using the Generalized Van Cittert – Zernike Schell Propagator”, co-authored by Manuel Montoya, Maria J. Lopera Acosta, Yunfeng Nie, and David Blinder.

On October 8th 2025 at 16:00, Xinxin Dai will defend their PhD entitled “LEARNING BASED RECONSTRUCTION AND MEASUREMENT OF 3D HANDS USING A SINGLE DEPTH CAMERA”.

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

Accurate 3D reconstruction and measurement extraction of the human hand are critical for a wide range of hand-centric applications, such as the design of immobilization devices, prosthetic limb fabrication, and osteoarthritis evaluation. However, the recovery of high-fidelity hand geometry remains challenging due to the inherently incomplete and occluded nature of point clouds acquired from commodity depth sensors, which are limited by viewpoint constraints and self-occlusion. Furthermore, traditional manual measurement methods, which require static hand postures and the expertise of trained anthropometrists, are inadequate for capturing measurements under realistic, task-specific hand motions, limiting their applicability in dynamic or non-standard scenarios.

To address these limitations, this thesis introduces deep learning-based methodologies aimed at addressing key challenges in the reconstruction and measurement extraction of 3D hand shapes. Specifically, the main research challenges include: (i) What is the optimal hand posture for precise and reliable measurement? (ii) How to fast and precisely reconstruct a complete hand shape from multi-view partial point clouds under different postures? and (iii) How can we simultaneously complete partial point clouds and reconstruct their surfaces while preserving the raw data? (iv) How to achieve human identification by the shape and posture of hands? The first challenge derives from the complexity of the human hand, which consists of 34 muscles and 27 bones. This intricate structure enables a wide range of postural variations, often resulting in significant geometric deformations that introduce considerable biases in measurement accuracy. Second, depth cameras inherently capture only partial point clouds due to limited viewpoints and self-occlusions, resulting in incomplete representations that restrict the accurate reconstruction of full hand geometry. Third, the lack of high-resolution surface details in a single partial point cloud makes it challenging to simultaneously achieve both point cloud completion and high-fidelity surface reconstruction. Lastly, while previous studies on human identification have primarily focused on recording the velocities of pressing and releasing different keys, these approaches lack integration with vision-based hand motion analysis.

To overcome the aforementioned challenges, this thesis introduces four deep learning-based models. The first model is  Measure4DHand, designed for automatic extraction of dynamic hand measurements from partial hand point cloud sequences. By analyzing the variation in measurement values induced by skin deformation across different hand postures, this model facilitates the identification of optimal hand postures for accurate and consistent measurements. The second model is PatientHandNet, which focuses on reconstructing a high-fidelity 3D hand shape in a canonical open-palm pose using four depth images captured from different viewpoints by a single commodity depth sensor To facilitate the proposed model, a large-scale multi-view synthetic dataset with a wide variety of hand shapes and hand poses and corresponding ground truth hand shapes in a canonical open palm pose is proposed and a novel real-world dataset by capturing 18 subjects (13 males and 5 females) via a structure sensor Mark I employed in an iPad and hired a professional anthropometrist to obtain corresponding ground-truth hand biometric measurements. The third contribution proposed TailoredTemplateFit, which is, to the best of our knowledge, the first deep learning-based method in the literature is proposed to simultaneously address point cloud completion and surface reconstruction while preserving the raw data of the input. This model is trained and validated on two large-scale datasets: a large-scale 50K head dataset and 300K hand dataset with a wide variety of shapes and poses and corresponding ground truth shapes. Lastly, we present KD-Net, which explores a novel visual modality of keystroke dynamics for human identification from RGB-D image sequences. To support this research, a novel dataset dubbed KD-MultiModal is created, comprising 243.2 K frames of RGB images and depth images.

Our proposed methods consistently outperform the reference methods from the literature, as demonstrated through comprehensive experimentation. The research works have been published in various reputable journals and conferences, highlighting their impact in both academic and industrial contexts.

On October 6th 2025 at 16:00, Ran Zhao will defend their PhD entitled “DEEP LEARNING-BASED HUMAN POSTURE NORMALIZATION AND AUTOMATIC ANTHROPOMETRIC MEASUREMENT”.

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

Accurate and user-friendly anthropometric measurement remains a major challenge in computer vision, as existing approaches typically require controlled scanning conditions, standard postures, or unclothed bodies. These constraints limit their usability in practical scenarios.

This thesis proposes a sequence of deep learning-based solutions to overcome these limitations. We first introduce OrienNormNet, an iterative network for robust orientation normalization, ensuring that scans are consistently aligned without manual preprocessing. Building on this, PoseNormNet is presented as the first posture normalization framework that transforms arbitrarily posed scans into a canonical T-pose while preserving identity details, removing the need for skeleton rigging. Next, W2H-Net demonstrates the feasibility of directly estimating the waist-to-hip ratio from partial dressed scans, showing that reliable indicators can be derived even from incomplete data. Finally, MeasureXpert provides a breakthrough toward real-world usability: it enables automatic extraction of anthropometric measurements from only two unregistered, partial, and clothed scans acquired in arbitrary poses.

To support these developments, the BWM dataset was synthesized for training, validation, and evaluation. Comprehensive experiments on both synthetic and real-world data confirm the effectiveness and robustness of the proposed methods. Collectively, the contributions progressively address key challenges related to cost, posture, and clothing, moving the field closer to practical, flexible, and accessible body measurement solutions.

The algorithms presented in this thesis have been disseminated through prestigious journals and conferences, demonstrating a modest yet meaningful impact on both academic research and industrial applications.

Non-EER staff needs a single permit, this request can take up to several months. The VUB requests this but the employee needs to deliver a large list of docs.

Karin needs to know if you want to hire someone new, or if the status of your researcher changes from PhD to post doc or researcher long in advance (3 M) to make sure all the steps are taken.

Karin cannot launch the procedure without a position and cannot create a position without budget (PKC) for at least 3 M

M&O – Wie vraagt de Single permit aan?

M&O – Wat gebeurt er als mijn statuut wijzigt? (Single Permit)

Redona Brahimetaj en Elena Botti hebben de best paper award gewonnen op het IWOAR 2025 congress met hun paper “Topological Versus Spatiotemporal Gait Parameters for Fall Risk Detection with IMU Sensors” (full author list: Redona Brahimetaj, Elena Botti, Ivan Bautmans, Eva Swinnen, Bart Jansen)

On September 22nd 2025 at 16:00, Thibaut Vandervelden will defend their PhD entitled “RUST-BASED IOT NETWORKS: A NETWORK PROTOCOL AND SECURITY PERSPECTIVE”.

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

The Internet of Things (IoT) continues to transform our interconnected world. Its rapid growth raises significant concerns about privacy and security. In recent years, numerous IoT botnets have exploited vulnerabilities in embedded devices to launch large-scale Distributed Denial of Service attacks. These attacks have caused significant disruption to internet services worldwide.

Many of these security vulnerabilities come from the use of memory-unsafe programming languages, such as C and C++. Programming languages with built-in safety features can mitigate these vulnerabilities. Since its first stable release in 2015, Rust has emerged as a popular choice for system programming, gaining popularity due to its unique combination of memory safety guarantees while keeping its performance comparable to the one obtained with traditional programming languages. Developers have successfully deployed Rust across diverse domains, including Operating Systems (OSs), web services, and embedded devices.

For IoT devices, two software components are particularly important: the OS and the network stack enabling communication. We provide an evaluation of OSs and frameworks for embedded devices available in Rust and examine their suitability for various IoT applications. We also investigate the feasibility and advantages of implementing a complete network stack in Rust for resource-constrained embedded devices. We built on the smoltcp library and extended it with a Rust implementation of IPv6 over Low-power Wireless Personal Area Networks (6LoWPAN). We studied and implemented the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). We developed and applied an evaluation methodology to check compliance with the standard and to verify interoperability with existing implementations. Doing so, we solved several issues of the current RPL implementation in ContikiNG. Our research also evaluates the effectiveness of 6LoWPAN generic header compression for use in RPL networks. The results demonstrate that this compression format significantly reduces energy consumption for IEEE 802.15.4-based networks that offer low data rates. For higher data rates, the benefit of this compression format diminishes.

Alongside contributing to networking components in Rust, we also explored security primitives. We evaluated the performance of different Keccak sponge constructions, cryptographic primitives that can serve as building blocks for various security protocols. Our findings reveal that Keccak sponge constructions with lower capacity parameters achieve significantly better efficiency on 32-bit microcontrollers commonly used in IoT devices. Building on these insights, we design and implement two novel symmetric-key-based authentication protocols. One tailored for wireless sensor networks and the other for fog based networks. We demonstrate that our protocol provides robust protection against known attacks while maintaining low computational and communication overhead.

Researchers at ETRO VUB have developed the 𝗧𝗿𝗮𝗻𝘀𝗶𝗲𝗻𝘁 𝗥𝗮𝗱𝗮𝗿 𝗠𝗲𝘁𝗵𝗼𝗱 (𝗧𝗥𝗠), a non-invasive tool to visualize what’s hidden inside our buildings. TRM can:

✅ Offer a clear picture of insulation in walls
✅ Identify reinforcement types inside concrete pillars
✅ Determine moisture levels in walls
✅ Conduct year-round thermal assessments
✅ Monitor real-time carbon uptake in building materials

The team is now seeking partners to help bring this innovation to the market and transition the construction industry toward a more sustainable future.

💡 Interested in a partnership?
Join the pitch session by Prof. Johan Stiens at the Klimaat Parlement 𝗶𝗻 𝗟𝗶𝗺𝗯𝘂𝗿𝗴 𝗼𝗻 𝟭𝟵 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿 𝟮𝟬𝟮𝟱 to learn more.
You can also contact him directly at jstiens@etrovub.be.

👉 Read all the details here: https://lnkd.in/exmkJ7CU

Ali Pourkazemi, Ashkan Zarghami Iliya Hakani

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.

“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)

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.

Loris Giordano got the best student paper award at the AMAI workshop of MICCAI 2025 for the paper “A modular deep-learning pipeline for automated aorta characterization on CT”, co-authored by Loris Giordano, Jakub Ceranka, Selene De Sutter, Kaoru Tanaka, Gert Van Gompel, Tom Lenaerts, and Jef Vandemeulebroucke.

Anass Hamdi got awarded an PhD fellowship strategic basic research for his research “AI-driven radiogenomic analysis for spatial glioblastoma subtyping” under supervision of Catharina Olsen, Jef Vandemeulebroucke, Johnny Duerinck and Wim Vranken.

Sarah Al Omari got awarded a PhD Fellowship fundamental research for her research “Exploring Neuromuscular Fatigue in Stroke Survivors: Central-Peripheral Interplay and the Potential of Transcranial Alternating Current Stimulation (tACS)” under supervision of Eva Swinnen, David Beckwée, Mahyar Firouzi and Bart Jansen.

Manuel Montoya received the best oral presentation award at the International Conference on Applied Physics & Imaging (ICAPI) 2025 in Tartu, Estonia, for the work “Efficient simulations of partially coherent light using the Generalized Van Cittert – Zernike Schell Propagator”, co-authored by Manuel Montoya, Maria J. Lopera Acosta, Yunfeng Nie, and David Blinder.

On October 8th 2025 at 16:00, Xinxin Dai will defend their PhD entitled “LEARNING BASED RECONSTRUCTION AND MEASUREMENT OF 3D HANDS USING A SINGLE DEPTH CAMERA”.

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

Accurate 3D reconstruction and measurement extraction of the human hand are critical for a wide range of hand-centric applications, such as the design of immobilization devices, prosthetic limb fabrication, and osteoarthritis evaluation. However, the recovery of high-fidelity hand geometry remains challenging due to the inherently incomplete and occluded nature of point clouds acquired from commodity depth sensors, which are limited by viewpoint constraints and self-occlusion. Furthermore, traditional manual measurement methods, which require static hand postures and the expertise of trained anthropometrists, are inadequate for capturing measurements under realistic, task-specific hand motions, limiting their applicability in dynamic or non-standard scenarios.

To address these limitations, this thesis introduces deep learning-based methodologies aimed at addressing key challenges in the reconstruction and measurement extraction of 3D hand shapes. Specifically, the main research challenges include: (i) What is the optimal hand posture for precise and reliable measurement? (ii) How to fast and precisely reconstruct a complete hand shape from multi-view partial point clouds under different postures? and (iii) How can we simultaneously complete partial point clouds and reconstruct their surfaces while preserving the raw data? (iv) How to achieve human identification by the shape and posture of hands? The first challenge derives from the complexity of the human hand, which consists of 34 muscles and 27 bones. This intricate structure enables a wide range of postural variations, often resulting in significant geometric deformations that introduce considerable biases in measurement accuracy. Second, depth cameras inherently capture only partial point clouds due to limited viewpoints and self-occlusions, resulting in incomplete representations that restrict the accurate reconstruction of full hand geometry. Third, the lack of high-resolution surface details in a single partial point cloud makes it challenging to simultaneously achieve both point cloud completion and high-fidelity surface reconstruction. Lastly, while previous studies on human identification have primarily focused on recording the velocities of pressing and releasing different keys, these approaches lack integration with vision-based hand motion analysis.

To overcome the aforementioned challenges, this thesis introduces four deep learning-based models. The first model is  Measure4DHand, designed for automatic extraction of dynamic hand measurements from partial hand point cloud sequences. By analyzing the variation in measurement values induced by skin deformation across different hand postures, this model facilitates the identification of optimal hand postures for accurate and consistent measurements. The second model is PatientHandNet, which focuses on reconstructing a high-fidelity 3D hand shape in a canonical open-palm pose using four depth images captured from different viewpoints by a single commodity depth sensor To facilitate the proposed model, a large-scale multi-view synthetic dataset with a wide variety of hand shapes and hand poses and corresponding ground truth hand shapes in a canonical open palm pose is proposed and a novel real-world dataset by capturing 18 subjects (13 males and 5 females) via a structure sensor Mark I employed in an iPad and hired a professional anthropometrist to obtain corresponding ground-truth hand biometric measurements. The third contribution proposed TailoredTemplateFit, which is, to the best of our knowledge, the first deep learning-based method in the literature is proposed to simultaneously address point cloud completion and surface reconstruction while preserving the raw data of the input. This model is trained and validated on two large-scale datasets: a large-scale 50K head dataset and 300K hand dataset with a wide variety of shapes and poses and corresponding ground truth shapes. Lastly, we present KD-Net, which explores a novel visual modality of keystroke dynamics for human identification from RGB-D image sequences. To support this research, a novel dataset dubbed KD-MultiModal is created, comprising 243.2 K frames of RGB images and depth images.

Our proposed methods consistently outperform the reference methods from the literature, as demonstrated through comprehensive experimentation. The research works have been published in various reputable journals and conferences, highlighting their impact in both academic and industrial contexts.

On October 6th 2025 at 16:00, Ran Zhao will defend their PhD entitled “DEEP LEARNING-BASED HUMAN POSTURE NORMALIZATION AND AUTOMATIC ANTHROPOMETRIC MEASUREMENT”.

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

Accurate and user-friendly anthropometric measurement remains a major challenge in computer vision, as existing approaches typically require controlled scanning conditions, standard postures, or unclothed bodies. These constraints limit their usability in practical scenarios.

This thesis proposes a sequence of deep learning-based solutions to overcome these limitations. We first introduce OrienNormNet, an iterative network for robust orientation normalization, ensuring that scans are consistently aligned without manual preprocessing. Building on this, PoseNormNet is presented as the first posture normalization framework that transforms arbitrarily posed scans into a canonical T-pose while preserving identity details, removing the need for skeleton rigging. Next, W2H-Net demonstrates the feasibility of directly estimating the waist-to-hip ratio from partial dressed scans, showing that reliable indicators can be derived even from incomplete data. Finally, MeasureXpert provides a breakthrough toward real-world usability: it enables automatic extraction of anthropometric measurements from only two unregistered, partial, and clothed scans acquired in arbitrary poses.

To support these developments, the BWM dataset was synthesized for training, validation, and evaluation. Comprehensive experiments on both synthetic and real-world data confirm the effectiveness and robustness of the proposed methods. Collectively, the contributions progressively address key challenges related to cost, posture, and clothing, moving the field closer to practical, flexible, and accessible body measurement solutions.

The algorithms presented in this thesis have been disseminated through prestigious journals and conferences, demonstrating a modest yet meaningful impact on both academic research and industrial applications.

Non-EER staff needs a single permit, this request can take up to several months. The VUB requests this but the employee needs to deliver a large list of docs.

Karin needs to know if you want to hire someone new, or if the status of your researcher changes from PhD to post doc or researcher long in advance (3 M) to make sure all the steps are taken.

Karin cannot launch the procedure without a position and cannot create a position without budget (PKC) for at least 3 M

M&O – Wie vraagt de Single permit aan?

M&O – Wat gebeurt er als mijn statuut wijzigt? (Single Permit)

Redona Brahimetaj en Elena Botti hebben de best paper award gewonnen op het IWOAR 2025 congress met hun paper “Topological Versus Spatiotemporal Gait Parameters for Fall Risk Detection with IMU Sensors” (full author list: Redona Brahimetaj, Elena Botti, Ivan Bautmans, Eva Swinnen, Bart Jansen)

On September 22nd 2025 at 16:00, Thibaut Vandervelden will defend their PhD entitled “RUST-BASED IOT NETWORKS: A NETWORK PROTOCOL AND SECURITY PERSPECTIVE”.

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

The Internet of Things (IoT) continues to transform our interconnected world. Its rapid growth raises significant concerns about privacy and security. In recent years, numerous IoT botnets have exploited vulnerabilities in embedded devices to launch large-scale Distributed Denial of Service attacks. These attacks have caused significant disruption to internet services worldwide.

Many of these security vulnerabilities come from the use of memory-unsafe programming languages, such as C and C++. Programming languages with built-in safety features can mitigate these vulnerabilities. Since its first stable release in 2015, Rust has emerged as a popular choice for system programming, gaining popularity due to its unique combination of memory safety guarantees while keeping its performance comparable to the one obtained with traditional programming languages. Developers have successfully deployed Rust across diverse domains, including Operating Systems (OSs), web services, and embedded devices.

For IoT devices, two software components are particularly important: the OS and the network stack enabling communication. We provide an evaluation of OSs and frameworks for embedded devices available in Rust and examine their suitability for various IoT applications. We also investigate the feasibility and advantages of implementing a complete network stack in Rust for resource-constrained embedded devices. We built on the smoltcp library and extended it with a Rust implementation of IPv6 over Low-power Wireless Personal Area Networks (6LoWPAN). We studied and implemented the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). We developed and applied an evaluation methodology to check compliance with the standard and to verify interoperability with existing implementations. Doing so, we solved several issues of the current RPL implementation in ContikiNG. Our research also evaluates the effectiveness of 6LoWPAN generic header compression for use in RPL networks. The results demonstrate that this compression format significantly reduces energy consumption for IEEE 802.15.4-based networks that offer low data rates. For higher data rates, the benefit of this compression format diminishes.

Alongside contributing to networking components in Rust, we also explored security primitives. We evaluated the performance of different Keccak sponge constructions, cryptographic primitives that can serve as building blocks for various security protocols. Our findings reveal that Keccak sponge constructions with lower capacity parameters achieve significantly better efficiency on 32-bit microcontrollers commonly used in IoT devices. Building on these insights, we design and implement two novel symmetric-key-based authentication protocols. One tailored for wireless sensor networks and the other for fog based networks. We demonstrate that our protocol provides robust protection against known attacks while maintaining low computational and communication overhead.

Researchers at ETRO VUB have developed the 𝗧𝗿𝗮𝗻𝘀𝗶𝗲𝗻𝘁 𝗥𝗮𝗱𝗮𝗿 𝗠𝗲𝘁𝗵𝗼𝗱 (𝗧𝗥𝗠), a non-invasive tool to visualize what’s hidden inside our buildings. TRM can:

✅ Offer a clear picture of insulation in walls
✅ Identify reinforcement types inside concrete pillars
✅ Determine moisture levels in walls
✅ Conduct year-round thermal assessments
✅ Monitor real-time carbon uptake in building materials

The team is now seeking partners to help bring this innovation to the market and transition the construction industry toward a more sustainable future.

💡 Interested in a partnership?
Join the pitch session by Prof. Johan Stiens at the Klimaat Parlement 𝗶𝗻 𝗟𝗶𝗺𝗯𝘂𝗿𝗴 𝗼𝗻 𝟭𝟵 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿 𝟮𝟬𝟮𝟱 to learn more.
You can also contact him directly at jstiens@etrovub.be.

👉 Read all the details here: https://lnkd.in/exmkJ7CU

Ali Pourkazemi, Ashkan Zarghami Iliya Hakani

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.