Doctoral School

Reading date: 11/05/2026

• AGUILAR PLAZAOLA, JOSÉ AGUSTÍN: DATA-DRIVEN MODELLING, STATE ESTIMATION, CELL CONTROL AND MOTION PLANNING FOR PEM FUEL CELL-POWERED VEHICLES
  
  Author: AGUILAR PLAZAOLA, JOSÉ AGUSTÍN
  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 AUTOMATIC CONTROL, ROBOTICS AND VISION
  Department: Institute of Robotics and Industrial Informatics (IRI)
  Mode: Normal
  Deposit date: 05/03/2026
  Reading date: 11/05/2026
  Reading time: 17:30
  Reading place: Aula 28.8, Av. Diagonal, 647, Escola Tècnica Superior d'Enginyeria Industrial de Barcelona (ETSEIB), Campus Sud, 08028 Barcelona
  Thesis director: HUSAR, ATTILA PETER | ANDRADE CETTO, JUAN
  Thesis abstract: This doctoral thesis presents novel advances in the areas of modeling, state estimation, path planning, and control to improve energy efficiency and durability of the powertrain of autonomous robots and electric vehicles driven by proton exchange membrane fuel cells. The main objective of the present work is to design and implement algorithms that, based on a thorough knowledge of the systems in question, improve the characteristics and outperform the state-of-the-art methods. Special emphasis is placed on testing the developed algorithms, as much as possible, with dynamic experimental profile dataIn the area of proton-exchange membrane (PEM) fuel cell modelling, a computationally efficient physical model is proposed. Next, a model with a structure based on neural networks, built exclusively from data, is developed and validated. This model is framed within a new paradigm of machine learning, the computation by reservoirs. Subsequently, a hybrid model is built, combining both the physical model and the data-driven model by means of a fusion algorithm based on radial basis functions. The three models are tested with a set of dynamic experimental data, and it is shown how the proposed hybrid structure outperforms each of the individual models.In the area of state estimation, a particle filter is developed with the objective of estimating internal states (or parameters) of the fuel cell, taking into account the nonlinearity of the system and the uncertainty in its model. The algorithm is capable of estimating the internal variables of a nonlinear system with non-Gaussian probabilistic distribution. The algorithm is implemented to estimate the exchange current density of a fuel cell and is tested with two sets of experimental data, outperforming two state-of-the-art estimation algorithms. The exchange current density estimation is then used to fit an auto-regressive model and predict the evolution of the stack voltage in a durability experiment.In the area of PEM fuel cell control, an architecture composed of a high-level controller is proposed, which is in charge of calculating optimal temperature values with the objective of minimizing the degradation of the catalyst layer of the PEM fuel cell and at the same time maximizing its performance. These optimum values are then sent to the local controller of the fuel cell temperature regulation system. The proposed controller is based on the model predictive control paradigm; for this, a multiobjective cost function is designed, based on state-of-the-art models of the platinum degradation process that occurs during stack operation. The controller is validated in simulation tests and shown how it can adapt the temperature according to load conditions, optimizing the performance of the catalyst layer and minimizing its degradation.In the area of path planning, a new planning algorithm is developed taking into consideration the degradation mechanisms in the catalyst caused by the cell voltage profile. The developed algorithm is an extension of the A* algorithm, including new cost and heuristic functions based on the latest degradation models available in the literature. These functions incorporate penalties related to the expected voltage profile in the routes that are more detrimental to the catalyst integrity. Simulation tests are performed with different scenarios and the performance of the developed path planner is compared with the conventional A* algorithm.In the area of energy efficiency control, a controller is developed with the objective of including energy optimization in an adaptive cruise control module. Each part of the controller is designed, including the system model, the cost function, and the constraints. A series of simulation tests are performed to compare the performance between the energy-optimized adaptive cruise controller and the conventional one.
  

• NOURMOHAMMADI, FARZANEH: Deep Learning Driven Blocking Prediction in Elastic Optical Networks using Spatio-Temporal Hybrid Neural Network
  
  Author: NOURMOHAMMADI, FARZANEH
  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: 24/03/2026
  Reading date: 11/05/2026
  Reading time: 11:00
  Reading place: UPC Campus Nord D4-012 Sala de Juntes TSC
  Thesis director: COMELLAS COLOME, JAUME
  Committee:
       PRESIDENT: RAFEL PORTÍ, ALBERT
       SECRETARI: GENE BERNAUS, JOAN MANUEL
       VOCAL: SHARIATI, MOHAMMAD BEHNAM
  Thesis abstract: The explosive growth of bandwidth-intensive services such as ultra-high-definition video streaming, cloud gaming, and virtual reality is pushing optical transport networks toward greater flexibility and efficiency. Elastic Optical Networks (EONs) address these demands by allocating spectrum in variable-sized frequency slots rather than fixed-grid channels. However, the dynamic setup and teardown of heterogeneous lightpaths fragment the optical spectrum, often leading to connection blocking even when sufficient total capacity exists. Accurate early prediction of blocking events enables network controllers to trigger proactive spectrum defragmentation and resource reallocation, improving service continuity and network utilization.This dissertation investigates deep learning-based approaches for blocking prediction in EONs using network state information represented as spectrum occupancy matrices. First, we introduce one-dimensional and two-dimensional Convolutional Neural Networks (1D-CNN and 2D-CNN) to automatically extract spatial fragmentation patterns from simulated EON topologies. The proposed 2D-CNN achieves a prediction accuracy of 92.17\% and outperforms conventional machine learning baselines such as Support Vector Machines and k-Nearest Neighbors. Building on this, we propose hybrid spatio-temporal architectures that integrate convolutional feature extraction with Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) units to capture both spatial and temporal evolution of spectrum fragmentation. The CNN–BiLSTM model achieves 94.1\% prediction accuracy while maintaining reasonable computational complexity, and the CNN–LSTM model offers a favorable trade-off between accuracy and training speed.By unifying spatial and temporal deep learning techniques, this work provides a comprehensive framework for early and reliable blocking prediction in dynamic EON environments. The proposed models can be integrated into elastic optical network controllers to enable timely defragmentation actions, reducing blocking probability and improving overall network efficiency. The findings advance the application of artificial intelligence to optical networking and lay the foundation for future integration with intelligent routing, spectrum assignment, and network automation strategies.
  

Reading date: 14/05/2026

• AREIAS FANZERES, LEONARDO: Sound-to-Image Translation Through Direct Cross-Modal Learning: An Exploratory and Architectural Study
  
  Author: AREIAS FANZERES, LEONARDO
  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: 16/04/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director:
  Thesis abstract: Environmental sound conveys rich semantic and contextual information about events, objects, and spatial dynamics. However, prevailing computational approaches to environmental audio analysis, such as Acoustic Event Detection (AED), typically reduce this complexity to discrete textual labels. While effective for automated monitoring tasks, such representations oversimplify acoustic scenes and become inadequate when auditory information must be communicated across modalities. Sound-to-image (S2I) translation offers an alternative approach in which a model synthesizes images that visually depict sound-emitting sources and their surrounding environments.This thesis introduces and advances direct sound-to-image translation, a paradigm that establishes a connection between audio and visual modalities without relying on textual mediation, class supervision, or cluster-based alignment during training. The central hypothesis is that higher-level abstractions learned by deep neural networks provide a shared semantic space in which heterogeneous modalities can connect directly, enabling the generation of images that are interpretable and semantically coherent with the source sound. Such outputs are referred to as informative, meaning that they visually communicate meaningful aspects of the acoustic event.The first part of the thesis presents, to the best of our knowledge, the first study dedicated to direct S2I translation. A densely connected generative adversarial network (GAN), conditioned on audio embeddings, is developed to synthesize images directly from sound. Because multiple plausible images may correspond to a single acoustic event, translation quality cannot be evaluated through pixel-level reconstruction. To address this challenge, an informativity-based evaluation framework is proposed, employing classifiers to determine whether generated images are interpretable and semantically coherent with the source audio. Experiments reveal that, despite the inherent difficulty of the task, the model generalizes to unseen sounds and produces informative outputs for a meaningful portion of translations. Analysis further reveals that latent bottleneck dimensionality influences translation behavior, exposing a trade-off between pixel-space convergence and informativity.Building on this foundation, the second part investigates whether attention mechanisms can strengthen cross-modal alignment. Self-attention and cross-attention modules are integrated into the generator and evaluated across multiple configurations. Results show that attention improves translation performance when applied at early stages of the network, increasing informativity relative to the purely convolutional baseline while preserving the fully direct audio–visual connection.Together, these contributions formally define, validate, and extend direct S2I translation as a distinct research paradigm. Beyond architectural design and training strategies, this thesis advances methodological principles for quantitative evaluation of cross-modal translation in the absence of deterministic visual ground truth. The findings contribute to a broader understanding of multimodal representation learning and highlight the potential of direct S2I translation for applications in multimodal interaction and accessibility-oriented technologies, particularly for enhancing situational awareness in deaf and hard-of-hearing individuals.
  

• BADAKHSHAN, EHSAN: Thermo-Hydro-Mechanical Modeling of Unsaturated Vegetated Slopes
  
  Author: BADAKHSHAN, EHSAN
  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 GEOTECHNICAL ENGINEERING
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Article-based thesis
  Deposit date: 16/04/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: VAUNAT, JEAN
  Thesis abstract: The top layer of soil on a slope is highly dynamic, influenced by atmospheric forces and the presence of vegetation. The vegetation condition plays a crucial role in determining the amount of water transpiration and evaporation from the slope surface, thus affecting slope performance by altering temperature, water content, and pore water pressure within the soil mass. Many numerical models often neglect the vegetation impact on soil performance. To capture these effects, this study employs a finite element model in CODE_BRIGHT that integrates vegetation and climate-driven interactions, aiming to bridge the gap between reality and simulation. A specialized boundary condition is developed to simulate soil–vegetation–atmosphere (SVA) processes with hydraulic hysteresis, linking canopy resistance to solar radiation, vapor pressure deficit, and soil saturation. Model validation using three years of field data from the Agropolis slope in Barcelona shows strong agreement, confirming its ability to reproduce vegetation effects on slope hydrothermal behavior. Results reveal pronounced daily temperature variations in the root zone, higher temperatures on south-facing slopes, and stronger drying under vegetation during hot, dry periods.Parametric analysis identifies leaf area index (LAI), root density, and vegetation fraction as key factors influencing soil moisture. Root density and LAI most strongly affect water retention, dense roots lower summer saturation by up to 40%, while high LAI reduces surface drying by 30%. Vegetation fraction enhances winter storage but intensifies summer drying.To better represent unsaturated soils, a hysteretic soil–water retention model is implemented in CODE_BRIGHT, coupling suction and void ratio changes to improve accuracy in scenarios such as rainfall-induced landslides.Finally, an enhanced Barcelona Basic Model (BBM-VEG) is formulated within a thermo-hydro-mechanical (THM) framework. The model introduces a strain-dependent reinforcement factor (Rpveg), correlated with root mass fraction and activation strain, allowing soil stiffness and strength to evolve dynamically. Validated with triaxial and tensile tests, it accurately captures the mechanical response of rooted soils. Slope simulations under hydraulic (infiltration rates of 0.001–0.003 and 0.0005 kg/s) and thermal (15–60 °C) cycles show that vegetation limits infiltration, enhances evapotranspiration, expands the unsaturated zone, and reduces deformation by up to 70% compared to bare slopes.
  

Reading date: 15/05/2026

• ARIAS CUEVAS, JOSÉ GABRIEL: Proyectos de recuperación de zonas vulnerables con materiales de ciclo cerrado. Casos de estudio, proyectos URBE.
  
  Author: ARIAS CUEVAS, JOSÉ GABRIEL
  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 ARCHITECTURAL, BUILDING CONSTRUCTION AND URBANISM TECHNOLOGY
  Department: Department of Architectural Technology (TA)
  Mode: Normal
  Deposit date: 05/03/2026
  Reading date: 15/05/2026
  Reading time: 10:00
  Reading place: EPSEB (Escuela Politécnica Superior de Edificación BCN) - Sala de ActosAv. Doctor Marañón, 44-50 - 08028 - Barcelona
  Thesis director: BOSCH GONZÁLEZ, MONTSERRAT
  Thesis abstract: The Dominican Republic, specifically Santo Domingo, has faced decades of unplanned urbanization in highly vulnerable areas, such as the banks of the Ozama River. This generates a socio-environmental risk intensified by overpopulation and a lack of specific public policies. Simultaneously, the absence of an integrated system for managing Construction and Demolition Waste (CDW), often disposed of improperly, causes critical environmental impacts. This research addresses the integration of CDW management and closed-loop materials into urban redevelopment projects promoted by the State in critical areas. Using La Nueva Barquita and Domingo Savio (URBE Projects) as case studies, the research serves as a starting point for future interventions in the country.The central objective is to propose construction alternatives to existing ones for urbanization projects in flood-prone areas through the systematic use of closed-loop materials and CDW recovery and valorization systems. It seeks to provide strategic knowledge so that these urban interventions can become "waste sinks," driving sustainable, socially committed, and viable development.The research follows a mixed methodology combining: a documentary study and critical analysis of local regulations; international references; and the state of the art regarding vulnerability and river intervention projects. It includes the analysis of the case study settlements, the construction sector and its main stakeholders, and the regulatory framework, alongside successful experiences of urban interventions using recycled products. Field research involved site visits and surveys of both residents of the Nueva Barquita project and key stakeholders in Santo Domingo's construction sector. Finally, a technical-economic analysis of work items was conducted using quantification tools such as TCQ/BEDEC. This triple approach allowed for a comprehensive understanding of local barriers and opportunities.The results are framed within three transformation vectors:Regulatory/Institutional Vector: Although a legal framework exists, technical instruments and specific contractual clauses are needed to integrate CDW management as a mandatory requirement in public works.Socio-Economic Vector: The research identified an active informal reuse market, demonstrating latent demand. Formalizing this sector can generate a new competitive and formalized economic fabric.Technical/Territorial Vector: The real feasibility of replacing conventional materials with CDW in redevelopment projects is validated, optimizing resilience on riverbanks through solutions combined with nature-based solutions (NBS).The findings demonstrate that while the Dominican State has a visible social commitment to relocating vulnerable populations, a critical gap persists in regulatory application and technical CDW integration. The main contribution of this thesis lies in having integrated a technical, regulatory, and territorial diagnosis that validates the hypothesis that urban projects can function as "waste sinks" in the Dominican context.The research establishes technical, institutional, and market foundations for the Dominican Republic to adopt a circular economy model in construction. The sector's robust growth presents an exceptional opportunity to implement this model, attracting sustainable investment and strengthening international competitiveness. This transforms the waste challenge into a strategic lever for resilient urban development and national economic prosperity.