On June 27th 2024 at 09:30, Cheng Chen will defend their PhD entitled “NOVEL FABRICATION AND ELECTROMAGNETIC-OPTICAL CHARACTERIZATION TECHNIQUES OF CARBON-BASED NANOSTRUCTURES”.

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

Nowadays, carbon nanomaterials are increasingly garnering attention as the next generation of semiconductor materials. Notably, graphene and carbon nanofibers (CNFs) have emerged as pivotal players in the semiconductor domain, owing to their remarkable electrical, mechanical, and thermal properties, coupled with their distinctive structural attributes.

Graphene, characterized by its two-dimensional single-layer structure of densely packed carbon atoms, boasts unparalleled electrical conductivity. This positions it for significant potential in applications like highfrequency electronic devices and sensors. Furthermore, its transparency and flexibility pave the way for innovative advancements in flexible electronic devices and display technologies, rejuvenating the electronics industry’s potential. CNFs, celebrated for their nanoscale diameter and exceptional mechanical attributes, carve a niche for themselves in material science. Their superior conductivity heralds vast opportunities, especially in realms such as conductive fibers and flexible circuitry. Within the spectrum of synthesis techniques, Chemical Vapor Deposition (CVD) emerges as a standout method, particularly for producing highquality graphene films and CNFs.

This dissertation delves into the CVD preparation, performance characterization, and subsequent applications of these materials, particularly in electromagnetic (EM) and ultraviolet (UV) optics. Specifically, the research encompasses:

1. Investigating the photoluminescence (PL) performance of ZnO/graphene composite films and ZnO@CNFs composite fibers within the UV emission spectrum
2. Determining the optimal thickness of ZnO in the ZnO/graphene composite for optimal UV-PL behavior
3. Analyzing the anisotropic EM loss behavior of CNFs and ZnO@CNFs samples across the MMW and sub-terahertz bands
4. Pioneering a novel quality-assessment method for wafer-scale CVDprepared graphene films, grounded in their distinctive interactions with terahertz TE10 waves

In essence, this research centers on graphene and CNFs, exploring their potential in the realm of EM and UV optics and offering insights based on their intrinsic properties.

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.

On June 21 2023 at 16.00, Nicolas Ospitia Patino will defend his PhD entitled “UNRAVELING TEXTILE-REINFORCED CEMENTITIOUS COMPOSITES BY MEANS OF MULTIMODAL SENSING TECHNIQUES”.

Everybody is invited to attend the presentation at the Room D.0.08.

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Textile Reinforced Cementitious (TRC) sandwich composites are innovative construction materials composed of two slender TRC facings, and a thick thermal and acoustic insulating core. Their non-corrosive nature allows for slender structures, resulting in a reduction of the cement used, and therefore a decrease of the negative impact on the environment. The sandwich technology brings superior bending resistance while enforcing the lightweight nature of the composite. Despite the numerous advantages of TRC sandwich composites, they present a complex and possibly unpredictable fracture behavior, and manufacturing issues such as a weak interlaminar bond and therefore, need status verification in the different stages of their service life: at manufacturing stage (curing), final product quality (manufacturing defects), deterioration during use (damage accumulation). Up to the moment, there is no reliable non-invasive inspection protocol that assesses the curing of the cementitious facings, provide quality control, and damage monitoring.

Along this study, a combination of Non-Destructive Testing (NDT) techniques is employed to provide a protocol that allows to monitor the composite from the hardening of the cementitious facings, quality control, and finally, damage characterization. Electromagnetic millimeter wave (MMW) spectrometry is employed for the first time in this kind of material to monitor the hydration of cementitious media, quality control, and damage characterization. Additionally, passive, and active elastic wave-based NDT techniques, like Acoustic Emission (AE) and Ultrasound, respectively, are also used in combination with Digital Image Correlation(DIC) to characterize the material along its lifetime, and benchmark MMW spectrometry. This thesis summarizes the results of an extensive experimental campaign, highlighting the innovative contributions. Previously unknown relations between electromagnetic properties measured by MMW and mechanical properties by ultrasound are revealed owing to the common origin of hydration reaction that dictates the permittivity and stiffness development. AE during proofloading reveals the effect of manufacturing defects due to the local stress field variations they impose under mechanical test. In addition, cracking and debonding leave a strong fingerprint on the electromagnetic transmission, enabling a multi-spectral methodology for the structural health monitoring (SHM) of such innovative components during their lifetime.

The Master in Applied Computer Science at VUB accepts students with a bachelor or Master in Engineering or Exact Sciences

On September 21 2022 at 10.00, Pengpeng Hu will defend his PhD entitled “Deep learning-based 3D human body shape reconstruction from point clouds”.

Everybody is invited to attend the presentation online via this link.

3D reconstruction of the human body shape is a fundamental problem in computer vision, which is valuable for various human-centric applications such as computer animation, virtual reality, and clothing design, to name a few. 3D scanning is a popular technology for acquiring the geometry of a subject based on which a 3D body reconstruction can be produced. Although countless body scanners were developed to meet different industrial requirements and a lot of advanced algorithms were proposed for optimizing the reconstructed body models, many problems are still not properly solved. These problems, however, are difficult to address using conventional methods.

Recent years have witnessed the rapid development of artificial intelligence, especially deep learning. Encouraged by the significant success of deep learning in image processing, an increasing number of researchers attempted to extend deep learning to deal with 3D data. Following this trend, we proposed deep learning-based solutions to several challenges existing in modern 3D body scanning and reconstruction.

In this thesis, we focus on four challenges of 3D body scanning, namely, (1) estimation of body shape under clothing, (2) body reconstruction from impaired point clouds, (3) registration of non-overlapping point clouds, and (4) animatable body reconstruction using a single depth camera. The first challenge arises from the fact that existing 3D scanning solutions require the subjects to get scanned with minimal clothing as the scanning device can only record the outmost surface of objects. This scanning procedure is inconvenient to most people and is also an infringement of the right to privacy. The second challenge comes from the observation that impaired point clouds are common in practice but they lack a systematic study. Moreover, the problems of misalignment and problematic posture are neglected in existing solutions. The third challenge is a classical problem: partial point cloud registration. We found that existing methods mainly rely on the assumption that the source and the target point clouds have sufficient overlap and none of them could handle non-overlapping registration. The last challenge is addressed as many applications demand dynamic human body models. Traditional methods require expensive professional devices to produce such models.

We have addressed these four challenges by leveraging the deep learning paradigm. Our first contribution is to propose the first deep learning-based method in the literature for estimating the body shape under clothing from a single 3D dressed body scan. To facilitate the proposed model, a novel dataset consisting of large-scale dressed body scans and corresponding ground-truth body shapes is proposed. Our second contribution is a novel deep learning approach for jointly reconstructing an accurate body mesh and normalizing the posture of the human body model from a low-quality body point cloud in arbitrary postures. It proposes to directly reconstruct high-fidelity body shapes from impaired point clouds instead of attempting to point cloud repairments. Our third contribution is the first deep learning-based method in the literature to align non-overlapping partial point clouds. Using this method, an omnidirectional body can be obtained from only two non-overlapping body scans. The last contribution in this thesis is to propose a novel deep learning-based method to reconstruct an animatable body shape from only two depth images and at the same time allow for large pose variations between the camera shots.

Extensive experiments based on different datasets have demonstrated that the proposed methods outperform the reference methods from the literature. Our work has resulted in numerous high-quality scientific publications and has demonstrated impact at both academic and industrial levels.