Doctoral School

Reading date: 22/07/2024

• HERKERT, EDIZ: Advanced Nanoantenna Platforms for Enhanced Single-Molecule Detection at High Concentrations
  
  Author: HERKERT, EDIZ
  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: 25/06/2024
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: GARCÍA PARAJO, MARÍA
  Committee:
       PRESIDENT: ACUNA, GUILLERMO
       SECRETARI: VAN HULST, NIEK
       VOCAL: ZIJLSTRA, PETER
  Thesis abstract: The ability to study the dynamics of individual biomolecules is crucial to understanding the complex organization of biological systems beyond what can be learned from ensemble averages. These single-molecule dynamics often occur at high micro- to millimolar concentrations, where conventional optical techniques cannot isolate single molecules anymore due to fundamental physical laws. This thesis explores the design, fabrication, and application of advanced nanoantenna platforms to detect individual fluorescent molecules at such high concentrations with increased sensitivity.Here, the theoretical groundwork is provided to understand the interactions between fluorescent molecules and nanoantennas. It is discussed how the single-molecule detection sensitivity of nanoantenna platforms can be quantitatively assessed through analytical models and numerical simulations. Based on these quantitative models, antenna-in-box platforms are identified to provide superior sensing performance and suitable lithography processes for their fabrication are established.Both computational and experimental evidence are presented that cleverly combining materials in hybrid antenna-in-box platforms enhances single-molecule detection sensitivity at micromolar concentrations. This improvement is attributed to decreased background signals and the use of previously unexplored coupling mechanisms inherent in the antenna-in-box architecture. Furthermore, hexagonal close-packed antenna-inbox platforms are introduced to enable highly parallelized single-molecule detection at micromolar concentrations. Notably, these hexagonally ordered platforms constitute the first demonstration of antenna-in-box platforms capable of single-molecule detection across the visible spectral range.Lastly, a correlative approach is presented that combines nonlinear fluorescence and vibrational spectroscopy to study the organization of receptor proteins in the cell membrane of living cells using nanoantennas. Measures to protect both the nanoantennas and the living cells are discussed and their effectiveness is validated.Overall, this thesis presents novel approaches for studying single-molecule dynamics at high concentrations with enhanced sensitivity. The development of these approaches was enabled through analytical and numerical modeling, the creation of new fabrication processes, and the use of appropriate experimental methods. These advancements promise to offer previously inaccessible insights into dynamics within biological systems.
  

• SHAABANZADEH, SEYEDEH SOHEILA: Contribution to the Development of Wi-Fi Networks through Machine Learning based Prediction and Classification Techniques
  
  Author: SHAABANZADEH, SEYEDEH SOHEILA
  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: 18/06/2024
  Reading date: 22/07/2024
  Reading time: 11:00
  Reading place: Aula de Teleensenyament, edifici B3, Campus Nord
  Thesis director: SANCHEZ GONZALEZ, JUAN
  Committee:
       PRESIDENT: ADELANTADO FREIXER, FERRAN
       SECRETARI: PEREZ ROMERO, JORGE
       VOCAL: KOUTLIA, KATERINA
  Thesis abstract: The growing number of Wi-Fi users and the emergence of bandwidth-intensive services have necessitated an increase in Access Point (AP) density, resulting in more complex network configuration, optimization, and management tasks. Concurrently, advancements in data monitoring and analytics technologies in wireless networks offer opportunities to extract valuable insights into network and user behavior, facilitating more efficient network management. In this thesis, we propose different Machine Learning based techniques to enhance Wi-Fi network management, focusing on three aspects: user connectivity prediction, Wi-Fi traffic prediction, and Wi-Fi traffic classification. The first aspect of our work focuses on predicting the next Access Point (AP) a user will connect to in a Wi-Fi network. We propose a methodology based on historical information of the AP to which a user has been connected, extracting connectivity patterns at different time scales (hourly, daily, weekly). Predictions are done using techniques based on Neural Networks and Random Forest algorithms. This approach is evaluated using real data from a university campus Wi-Fi network. Predicting the next AP of users allows for proactive network reconfiguration, enhancing the efficiency of techniques like Pairwise Master Key caching and Opportunistic Key Caching, which reduce re-authentication times. Additionally, this prediction helps to identify the geographical region of the User Equipment (UE) and can be used for commercial purposes, such as targeted advertising, by customizing messages based on locations of the users. Secondly, we propose a methodology for predicting the aggregated traffic at access points (APs) by leveraging spatial and temporal correlations from neighboring APs to enhance prediction accuracy. Using real measurements, we evaluate various Deep Learning methods, including Convolutional Neural Network (CNN), Simple Recurrent Neural Network (SRNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Transformer, and present a hybrid approach combining CNN for spatial processing and RNN for temporal prediction. This hybrid method improves accuracy with minimal additional training time and negligible impact on prediction time. Accurate traffic forecasting at each AP enables better load distribution and can inform resource management techniques such as admission control, congestion control, and load balancing. Additionally, predicting low traffic periods can aid in energy-saving strategies by allowing APs with minimal traffic to be switched off during specific times. Finally, traffic classification is essential for enhancing network performance by allowing better resource allocation and prioritization of services with stringent latency requirements. The increasing demand for Virtual Reality (VR) services poses a significant challenge for Wi-Fi networks to meet strict latency needs, crucial for VR to ensure immediate response and avoid user discomfort. To improve VR Quality of Service (QoS), distinguishing interactive VR traffic from Non-VR traffic is key. We propose a machine learning-based method to identify interactive VR traffic in a Cloud-Edge VR environment by analyzing downlink and uplink data correlations and extracting features from single-user traffic characteristics. Six classification techniques (i.e., Logistic Regression, Support Vector Machines, k-Nearest Neighbors, Decision Trees, Random Forest, and Naive Bayes) are compared. The result of the classification is used for the prioritization of VR traffic over Non VR traffic. We evaluate our method using datasets from various VR applications and Wi-Fi network simulations. Our results show a significant reduction in VR traffic delays with minimal impact on Non-VR service latency.
  

• VINYETA MEDINA, GIL: Modelo de priorización de inversiones de soluciones tecnológicas en Smart Buildings con criterios de desarrollo sostenible.
  
  Author: VINYETA MEDINA, GIL
  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 CONSTRUCTION ENGINEERING
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 15/05/2024
  Reading date: 22/07/2024
  Reading time: 11:00
  Reading place: Defensa pública a l'Aula 28.8 (ETSEIB) Av. Diagonal, 647 08028 Barcelona - Campus Sud UPC
  Thesis director: CUERVA CONTRERAS, EVA | PUJADAS ÁLVAREZ, PABLO
  Committee:
       PRESIDENT: ARMENGOU ORÚS, JAUME
       SECRETARI: PARDO BOSCH, FRANCESC
       VOCAL: ECHARRI IRIBARREN, VICTOR
  Thesis abstract: This Doctoral Thesis addresses aspects of interest for designers, promoters, owners, and managers of office buildings, with the intention of providing clarity on the Smart Building paradigm, as well as identifying decision support tools necessary to accelerate technological transformation, without underestimating the sustainable development, in the building sector.The main goal is to develop a new methodology for prioritizing investments in technological solutions for Smart Buildings, enabling an objective, transparent, traceable, and participatory distribution using sustainable development criteria throughout the various stages of a building's life cycle. To develop this the methodology three tools need to be created: (1) the Catalogue of technological solutions and (2) the Model for prioritizing technological solutions, both as decision support tools for the design and conceptualization phases, and (3) the Platform for integrating technological solutions to improve asset management during the operation phase.Firstly, a methodology for analyzing and classifying the universe of existing technological solutions applicable to the building sector is developed, with the aim of identifying their true potential and implementation requirements. This information is crucial for subsequently selecting the most suitable solutions for each project.Secondly, once the solutions are catalogued, it is essential to prioritize actions to achieve the desired Smart Building level and ensure meeting the real needs of the client and the end-users who will interact with the building. Therefore, a multicriteria model is generated to prioritize the technological solutions based on sustainable development criteria, involving end-users while relying on sector reference standards. To verify the robustness of this model, a sensitivity analysis is conducted.Thirdly, after the technological solutions are prioritized and selected, the implementation of a foundational, modular, flexible and scalable platform is necessary to ensure comprehensive management of all implemented technological solutions, both present and future. This platform aims to collect, standardize, analyze, and visualize all generated data centrally, optimizing the overall building performance.Finally, the functionality of the developed tools has been verified through two real practical applications. First in a more confined scope, such as the offices of JG Ingenieros, and then in a project for a real JG client, Simon's new corporate headquarters, SWITCH.
  

• YUN, HAORAN: Real-time Avatar Animation Synthesis in Virtual Reality
  
  Author: YUN, HAORAN
  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: 25/06/2024
  Reading date: 22/07/2024
  Reading time: 11:00
  Reading place: FIB, Sala d'actes Manuel Martí Recober, edifici B6, planta 0
  Thesis director: PELECHANO GOMEZ, NURIA | ANDUJAR GRAN, CARLOS ANTONIO
  Committee:
       PRESIDENT: ARGELAGUET SANZ, FERNANDO
       SECRETARI: SUSIN SANCHEZ, ANTONIO
       VOCAL: MALPICA MALLO, SANDRA
  Thesis abstract: The rapid development of consumer-grade virtual reality (VR) systems has changed how users interact within virtualenvironments. This opens up exciting opportunities for various domains, such as education, social interactions, and gaming.However, the current VR technology often provides only head and hand tracking, which significantly restricts the potential foroffering realistic full-body animation to create immersive VR experiences. This is especially problematic for activities in VR thatrequire users to use their whole body, like human factors engineering, fitness, rehabilitation, and training. In addition to the sparsetracking limitation, the field faces many other significant challenges, including the real-time performance requirement, not havingenough VR motion datasets, and challenges regarding how we evaluate these technologies.Therefore, as technical contributions, this thesis presents two novel data-driven solutions for full-body avatar animation in VRfrom sparse tracking data. The first method breaks down the animation process into three parts: body orientation, lower body, andupper body, solving them by different modules. A lightweight neural network is used to estimate the body direction from theHead-Mounted Display (HMD) and controllers. Then, customized Motion Matching finds the motion from a dataset that bestmatches the user's movement, avoiding fixed walking animations. Inverse kinematic solvers are used to animate the upper bodyand to refine the final pose. The second method uses a novel deep-learning framework to reconstruct the full-body motion from thepositions and rotations of six tracking devices without separating different body parts. Once trained, the model takes live datafrom VR devices and outputs an accurately animated full-body avatar that the user can control as their physical body. We alsocaptured several datasets featuring interaction movements and locomotion most relevant to VR avatar animation which have beenmade publicly available.In addition to its technical advancements, this thesis also contributes to a better understanding of user experiences in virtualreality through user studies. Two in-depth user studies have been conducted to evaluate the impact of animation quality andcollision feedback on how users perceive and interact within VR environments. Through objective metrics, subjectiveassessments, and interviews, insights were gained into improving avatar animation and virtual interactions. Findings include thatsparse trackers with high-quality inverse kinematics can match the embodiment of advanced motion capture suits for certain tasksbut fall short for tasks requiring accurate poses. Moreover, our studies indicate that achieving a realistic interaction with othervirtual humans requires not only advanced animation methods but also believable collision feedback, such as inducing participantsto expect that a physical bump against a real person might occur. Together, this research has advanced the state-of-the-art infull-body avatar animation in VR and deepened our understanding of potential improvements in this field.
  

Reading date: 23/07/2024

• CAMPOS SALAZAR, JOSE MANUEL: Design and Analysis of Battery Chargers for Electric Vehicles Based on Multilevel Neutral-Point-Clamped Technology
  
  Author: CAMPOS SALAZAR, JOSE MANUEL
  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 ELECTRONIC ENGINEERING
  Department: Department of Electronic Engineering (EEL)
  Mode: Normal
  Deposit date: 30/05/2024
  Reading date: 23/07/2024
  Reading time: 11:00
  Reading place: ETSEIB: Defensa pública a la Sala de Presentacions 28.8-Avda. Diagonal, 647-Campus Sud, Barcelona
  Thesis director: BUSQUETS MONGE, SERGIO | FILBÀ MARTÍNEZ, ÀLBER
  Committee:
       PRESIDENT: TRILLA ROMERO, LLUÍS
       SECRETARI: BORDONAU FARRERONS, JOSE
       VOCAL: BARAIA-ETXABURU ZUBIAURRE, IGOR
  Thesis abstract: This thesis presents an exploration into the field of advanced battery charger design and control, addressing critical needs across a spectrum of modern applications. It first outlines the increasing importance of battery chargers in various domains, emphasizing the requirements of efficiency, adaptability, and reliability. A detailed review of existing technologies and control strategies underscores the urgent need for innovation in charger design.The focus of this research is the design and development of a battery charger topology. This topology is based on cascaded multilevel converters that provide bidirectional power flow and galvanic isolation. It addresses the charging requirements of multiple batteries connected in series and incorporates two distinct dc links. The charger develops from a three-level configuration to a four-level configuration, finally leading to a generalized n-level charger.Integral to this work is the formulation of comprehensive linear models, from state-space to s-domain representations, which highlight the charger's complex dynamics. This allows for an in-depth understanding of its operational behavior and control characteristics.The thesis also introduces a well-tuned control system that synchronizes the operation of the two multilevel converters. This ensures optimal operation of the charger. The ac-dc converter regulates the dc link voltage and grid power factor, while the dc-dc converter regulates the shared dc link voltage and battery bank charging currents. The user has the flexibility to determine which converter controls the dc-link voltage regulation.A key innovation is the independent charge control for each battery bank. This feature allows batteries to reach full charge independently, regardless of their initial state of charge or rated capacity. This improves overall efficiency and battery management.In addition, the thesis provides a practical and straightforward design methodology for compensators based on the charger's linear schemes. It employs uncompensated gain loops and uses Bode plots for effective tuning of compensator parameters.Finally, the thesis outlines future research directions. These include experimental validation, exploring vehicle-to-grid integration, exploring nonlinear control systems, assessing off-board charger viability, examining renewable energy integration, enhancing grid-supporting features, evaluating scalability and efficiency, and evaluating real-world applications. Together, these efforts promise to advance and optimize the proposed battery charger, placing it as a central element in the field of efficient and sustainable energy systems.