Doctoral School

Reading date: 20/11/2025

• MUTHINENI, KARTHIK: Wireless Infrastructure-Based Indoor Positioning in Controlled Industrial Environments
  
  Author: MUTHINENI, KARTHIK
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS
  Department: Department of Signal Theory and Communications (TSC)
  Mode: Normal
  Deposit date: 16/10/2025
  Reading date: 20/11/2025
  Reading time: 16:00
  Reading place: Aula Teleensenyament, Edifici B3 - Ricardo Valle Sala 103 Planta 1
  Thesis director: VIDAL MANZANO, JOSE | ARTEMENKO, ALEXANDER | NAJAR MARTON, MONTSERRAT
  Thesis abstract: Wireless communications have become the central nervous system of the Factory of the Future (FoF). According to this, wireless infrastructure in industries serves the dual purposeof providing connectivity and positioning industrial assets such as Automated Guided Vehicles (AGVs). The Non-Line-of-Sight (NLoS) and multipath-dominant environments, such asthose found in industries, hinder wireless signal propagation, leading to inaccuracies in wireless infrastructure-based positioning. While advances have been made in developing approaches to enhance wireless positioning accuracy in complex indoor scenarios, they may not be readily applicable to industrial environments without or with minimal modification. This motivates the study towards developing accurate and precise wireless infrastructure-based positioning approaches tailored explicitly for industrial settings. This PhD thesis aims to address key challenges in wireless positioning for industrial environments and develop algorithms to enhance the accuracy of wireless infrastructure-based positioning by proposing both model-driven and data-driven approaches.This PhD thesis presents simulation analysis and practical experiments to validate the efficiency of the proposed approaches using a range of different wireless infrastructures, including Fifth Generation (5G), Ultra-Wideband (UWB), and Sixth Generation (6G) mobile communication, including Integrated Sensing and Communication (ISAC). This work begins by analyzing the achievable 5G-based positioning accuracy in C-band and Millimeter-Wave (mmWave) bands within a specific model of a dense, cluttered industrial environment, utilizing high-fidelity ray tracing simulations to capture the impact of NLoS and multipath propagation. The findings highlight limitations of 5G positioning and, in general, wireless positioning in complex and cluttered industrial settings. To overcome these limitations, this work advances the State of the Art (SotA) by developing novel enhancement approaches based on sensor and/or data fusion for UWB-based and ISAC-assisted positioning systems. These approaches leverage the power of Deep Neural Networks (DNNs), Long Short-Term Memory (LSTM) networks, and Graph Neural Networks (GNNs) to model spatial and temporal relationships more effectively, yielding substantial improvements in positioning accuracy, robustness, and adaptability to dynamic industrial scenarios. Furthermore, the application of the proposed approaches is illustrated through an Automated Guided Vehicle (AGV) use case. This PhD thesis lays a strong foundation for advancing future research and real-world applications, offering valuable insights that can shape the next generation of industrial wireless positioning system design and deployment.
  

• TIRADO GUTIERREZ, RODOLFO JAVIER: EVALUATION OF STRUCTURAL RELIABILITY. REVIEW OF DESIGN METHODOLOGIES AND SEISMIC PERFORMANCE EVALUATION OF REINFORCED CONCRETE STRUCTURES
  
  Author: TIRADO GUTIERREZ, RODOLFO JAVIER
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 25/09/2025
  Reading date: 20/11/2025
  Reading time: 16:00
  Reading place: ETSECCPB.UPC, Campus NordBuilding C1. Classroom: 002C/Jordi Girona, 1-308034 Barcelona
  Thesis director: VARGAS ALZATE, YEUDY FELIPE | GONZALEZ DRIGO, JOSE RAMON
  Thesis abstract: Seismic risk and structural reliability are fundamental concepts in the design and evaluation of safe and resilient buildings. It should be noted that most of the casualties, injuries and economic losses during an earthquake are associated with damage to civil structures. In this context, there is an urgent need to improve the design and evaluation methodologies of this type of structures. From a numerical perspective, several strategies can address this need, ranging from improvements in modeling and advanced analysis methods, to the probabilistic analysis of the variables involved (such as seismic hazard), including the review of configuration and material properties of the systems under study. Therefore, this research proposes a methodology to evaluate structural reliability using a probabilistic approach, validated through nonlinear dynamic analysis and a statistical cloud study. This methodology constitutes a robust and powerful tool, applicable not only at the building level but also at urban and regional scales. For this purpose, it is proposed to study a set of reinforced concrete buildings with variable configurations in both plan and elevation. These models represent real buildings, that were recently designed and constructed, located in areas of high seismicity in Colombia, for which the most modern seismic-resistant design standards have been followed. This thesis is divided into three main sections: 1) Calculation of improved intensity measures, in terms of efficiency and steadfastness, to derive more accurate fragility curves; 2) Development of a probabilistic analysis methodology to estimate, with high statistical accuracy and reduced time, the dynamic response of tall buildings by using transfer functions; and 3) Evaluation of structural reliability, based on the probability of exceeding different damage indices and thresholds at different levels of seismic intensity. The first part focuses on identifying and developing optimal and improved intensity measures, based on the efficiency and steadfastness they show as correlated with the structural response of complex systems. An optimal seismic intensity measure enables the development of more accurate fragility curves, which are essential for assessing the probability of damage in a structure under different seismic intensity levels. The second part focuses on the development of a structural analysis method based on the transfer function (TF) concept. This mathematical model establishes the relationship between the response of a system and the input excitation. The proposed approach allows probabilistic estimation, while maintaining statistical accuracy, of the nonlinear dynamic response. It aims to overcome the limitations of the high computational cost associated with nonlinear dynamic analysis. Finally, the third part focuses on calculating the structural reliability of two real buildings located in a high seismicity zone, evaluating the probability of exceeding different damage thresholds. The results obtained show that intensity measures based on velocity present a higher correlation with the structural response, regardless of whether they are analyzed as a whole. This will make it possible to evaluate risk scenarios in large areas by means of fragility curves that adequately represent different structural typologies, facilitating a better characterization of urban environments. Likewise, it is observed that the developed method, by using a reduced number of seismic records, allows obtaining reliable results in terms of the principal statistical moments of the structural response of complex systems, and importantly, in a considerably shorter time. In conclusion, this research presents a series of advanced numerical tools that allow the calculation of damage scenarios, while optimally accounting for seismic hazard, and using methodologies that significantly reduce the time required to estimate the structural reliability of a set of buildings.
  

Reading date: 21/11/2025

• MORALES CURIEL, LUIS FELIPE: Deep-learning enhanced bioluminescence microscopy
  
  Author: MORALES CURIEL, LUIS FELIPE
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN PHOTONICS
  Department: Institute of Photonic Sciences (ICFO)
  Mode: Normal
  Deposit date: 16/10/2025
  Reading date: 21/11/2025
  Reading time: 10:00
  Reading place: ICFO Auditorium
  Thesis director: KRIEG, MICHAEL
  Thesis abstract: Bioluminescence microscopy presents a powerful alternative to fluorescence imaging by eliminating the need for external illumination, thereby avoiding issues such as phototoxicity, photobleaching, and background autofluorescence. However, the inherently low photon output of luciferase-based reporters significantly restricts the signal-to-noise ratio (SNR), as well as the achievable spatial and temporal resolution—challenges that are especially pronounced in dynamic or volumetric biological imaging. This thesis addresses these limitations by introducing a deep learning-driven imaging pipeline designed to enhance bioluminescence microscopy at both the data acquisition and image reconstruction stages.Our strategy integrates optical system design with advanced neural networks to enable rapid, high-resolution 3D imaging under extremely low-light conditions. We engineered a custom microscope featuring a highly compact optical axis and paired it with a single-photon sensitive camera, significantly boosting the SNR of bioluminescent images. To achieve fast volumetric imaging, we incorporated light field microscopy (LFM) and Fourier light field microscopy (FLFM), enabling single-shot 3D acquisition while improving axial and lateral resolution via Fourier-domain filtering. The primary objective of this work is to demonstrate how deep learning can substantially enhance bioluminescence microscopy, pushing the technique beyond its traditional limits in both 2D and 3D imaging.At the core of our approach is a suite of convolutional neural networks specifically trained on bioluminescent data. Using both synthetic and experimental datasets, we designed and trained models capable of extracting meaningful information from low-SNR raw data, recovering otherwise lost details and offering deeper insight into the biological sample. The models developed in this thesis cover key tasks such as denoising and reconstruction of wide-field, light field, and Fourier light field bioluminescent images. Together, they form a modular, learnable pipeline that significantly elevates the performance of bioluminescence microscopy in terms of both quality and speed.We validate our system using live biological samples, including Caenorhabditis elegans, mouse stem cells, and zebrafish embryos, capturing neuronal activity and intracellular dynamics at subsecond timescales. By placing deep learning at the heart of the imaging process, this work establishes a new paradigm for bioluminescence microscopy, transforming a traditionally low-SNR modality into a robust tool for fast, high-resolution, and label-specific imaging in living organisms.
  

• PEDRAGOSA BATLLORI, GEMMA: Santa Coloma d'Andorra: el projecte d'una església a l'Andorra d'abans del S.XI.
  
  Author: PEDRAGOSA BATLLORI, GEMMA
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN THEORY AND HISTORY OF ARCHITECTURE
  Department: Department of History and Theory of Architecture and Communication Techniques (THATC)
  Mode: Normal
  Deposit date: 01/09/2025
  Reading date: 21/11/2025
  Reading time: 10:00
  Reading place: ETSAV. Campus de Sant Cugat del Vallès Carrer Pere Serra, 1-15 - 08173 Sant Cugat del Vallès Aula: Seminari 2
  Thesis director: GRANELL TRIAS, ENRIQUE | GINER OLCINA, JOSEP
  Thesis abstract: The church of Santa Coloma d’Andorra belongs to one of the simplest and oldest architectural types of religious architecture: that of a single rectangular nave with a square apse. However, the simplicity of this type should not necessarily be associated with a straightforward or immediate construction or design.The aim of this study is to determine the extent to which the architecture of Santa Coloma follows a complex metrical design, which could only be achieved within a cultural context that, in Santa Coloma — located near two major cultural centres of the time, the Cathedral of La Seu d’Urgell and the Monastery of Sant Serni de Tavèrnoles — is highly plausible.In this work, architecture is used as archaeological material to analyse the key elements of the building’s architectural composition. Historiography has been reviewed, plans have been drawn up, the unit of measurement has been identified, and its dimensions studied in relation to the knowledge of proportion of the period and descriptions of biblical buildings. And it turns out that in order to conceive, design and build an apparently simple church like this, it was necessary to be familiar with the architecture represented in the Bible and with the arithmetical elaborations compiled by Boethius and Cassiodorus in the 6th century.We will therefore see a building which, although rural and seemingly modest, is the result of a layout and proportions based on a specific symbolic language, reflecting and documenting a body of knowledge and a way of applying it.
  

• PONTÓN MARTINEZ, JOSE LUIS: Learning Data-driven Character Animation for Avatars in Virtual Reality
  
  Author: PONTÓN MARTINEZ, JOSE LUIS
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN COMPUTING
  Department: Department of Computer Science (CS)
  Mode: Normal
  Deposit date: 27/10/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: ANDUJAR GRAN, CARLOS ANTONIO | PELECHANO GOMEZ, NURIA
  Thesis abstract: The accelerating trend of remote interaction, driven by globalization and digital communication, underscores the need for richer, more immersive virtual collaboration. While current 2D video platforms enhance communication, Virtual Reality (VR) offers the unique potential for truly natural 3D interaction. Accomplishing this, however, critically depends on accurately representing human motion and achieving presence within virtual environments.This thesis addresses the challenge of achieving real-time, high-fidelity, and perceptually natural full-body self-avatar animation within VR environments using consumer-grade tracking devices. Accurate self-avatars are fundamental for inducing a strong Sense of Embodiment and enabling effective non-verbal communication, yet current methods often struggle with the inherent sparsity and variability of available sensor data.We first address fundamental aspects of animation fidelity and perceptual realism, and introduce methodologies for precise avatar skeleton adjustment, which significantly mitigate issues arising from mismatches between a user's physical proportions and their virtual representation. We also study various interaction metaphors to minimize visual discrepancies between real controllers and virtual hands, thereby enhancing user embodiment and task performance. These studies underscore the importance of accurate animation and lay the groundwork for learning-based approaches to achieve natural and temporally coherent motion from sparse inputs, overcoming the limitations of traditional inverse kinematics.Building upon these insights, the thesis explores the development of data-driven reconstruction methods that can handle diverse and ambiguous sensor inputs. We propose a novel deep learning-based system that accurately reconstructs full-body poses from minimal consumer-grade VR trackers, effectively addressing the underdetermined nature of this problem. Recognizing the inherent one-to-many mapping problem in sparse input, where a single input can correspond to multiple plausible poses, we then explore the potential of generative AI. Our work demonstrates how Variational Autoencoders (VAEs) can enable fine-grained control and adaptability to variable sensor configurations through latent space optimization, while diffusion models facilitate multimodal reconstruction from novel sensor types, such as pressure-sensing insoles.