Institution profile

Universidad Politécnica de Madrid

Academic institutioneurope · es
Official website
Research library304linked papers
Opportunities0open roles
Selected work

Representative Papers

Measurement-Based Modeling and Analysis of UAV Air-Ground Channels at 1 and 4 GHz

Jul 23, 2019IEEE Antennas and Wireless Propagation Letters

Existing vertical-dimension channel models for unmanned aerial vehicle (UAV) air-to-ground communications suffer from insufficient accuracy, particularly in characterizing height-dependent propagation effects. Method: This study conducts extensive field measurements at 1 GHz and 4 GHz across line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, systematically quantifying large-scale path loss, shadow fading (modeled via log-normal distribution), and small-scale fading (validated against Rayleigh and Rician distributions). Contribution/Results: We propose, for the first time, a height-dependent path loss model that explicitly incorporates UAV flight altitude as a key parameter, and jointly characterize vertical-direction propagation specificity and fading statistics. The resulting high-fidelity air-to-ground channel model significantly improves link budget prediction accuracy and coverage performance assessment reliability. It provides a reproducible, scalable empirical foundation and modeling paradigm for low-altitude communication network design and optimization.

49 citations2 influentialRead paper

SARA: A Microservice-Based Architecture for Cross-Platform Collaborative Augmented Reality

Mar 19, 2020Applied Sciences

Current AR applications face two major bottlenecks: redundant cross-platform development and the absence of robust multi-user collaboration mechanisms. To address these challenges, this paper proposes SARA—the first scalable microservice architecture designed specifically for collaborative augmented reality. SARA achieves full decoupling of communication protocols, data models, and device orchestration, thereby completely separating collaboration logic from underlying platform dependencies. It introduces a novel plugin-based design grounded in abstract collaboration models—such as turn-taking, ownership, and hierarchy—enabling dynamic definition and reuse of new collaboration semantics. SARA integrates Unity/MRTK and iOS ARKit to support real-time synchronization across heterogeneous devices, including HoloLens and iOS platforms. Evaluated via a voxel-based collaborative game prototype, SARA demonstrates feasible low-latency multi-device collaboration, improves collaboration logic development efficiency by 60%, and achieves an 85% model component reuse rate.

10 citationsRead paper

Enhancing Interaction with Augmented Reality through Mid-Air Haptic Feedback: Architecture Design and User Feedback

Nov 26, 2019Applied Sciences

This work addresses the spatial misalignment challenge between haptic feedback and virtual objects in augmented reality (AR). We propose the first flexible, cross-platform architecture enabling deep integration of mid-air haptics (Ultrahaptics) with AR, supporting HoloLens, iOS, and diverse haptic devices—including wearable, grasp-based, and ultrasonic mid-air systems. Our approach leverages AR spatial registration, haptic-visual synchronized rendering, and cross-device semantic mapping to achieve high-fidelity haptic representation and spatial consistency of virtual objects. User studies demonstrate that mid-air haptics significantly improves shape recognition accuracy (+32%) and scaling task completion rate (+41%). We validate the architecture’s feasibility through two applications—Form Inspector and Simon Game—and uncover systematic user expectation mismatches regarding haptic metaphors (e.g., virtual buttons), thereby informing the evolution of haptic AR interface design principles.

9 citations1 influentialRead paper

Towards resilient cities: A hybrid simulation framework for risk mitigation through data-driven decision making

Mar 01, 2024Simulation modelling practice and theory

Existing urban risk management systems suffer from incomplete coverage, insufficient coupling of heterogeneous operational data sources, and the absence of unified evaluation criteria. To address these challenges, this study proposes a hybrid simulation-based decision support framework integrating physics-informed modeling with data-driven methodologies. The framework introduces an innovative multi-scale coupled simulation architecture that enables real-time co-modeling of infrastructure network dynamics and socio-behavioral data. It synergistically integrates system dynamics, graph neural networks, Bayesian optimization, and digital twin technologies to support closed-loop decision-making for risk identification, propagation simulation, and resilience enhancement. Validated across three pilot cities, the framework achieves a 42% reduction in disaster response latency and a 31% decrease in recovery time for critical infrastructure. This work establishes a scalable, interpretable, and quantitatively evaluable technical paradigm for risk governance in complex urban systems.

7 citationsRead paper

Application of Deep Reinforcement Learning to UAV Swarming for Ground Surveillance

Oct 27, 2023Italian National Conference on Sensors

This study addresses the challenges of collaborative efficiency and robustness in multi-UAV swarm operations for ground security surveillance—specifically target search, localization, and persistent tracking. We propose a hierarchical hybrid AI architecture: a centralized swarm controller orchestrates high-level task allocation, while each UAV executes role-specific sub-agents for search, localization, and tracking. Notably, we introduce the first task-role-differentiated deployment of Proximal Policy Optimization (PPO) reinforcement learning models across the swarm and design a surveillance-oriented evaluation metric suite. Simulation results demonstrate efficient area coverage, rapid target acquisition, high tracking stability (interruption rate < 2%), and low-latency response. The framework balances technical sophistication with pedagogical interpretability, making it suitable for AI and robotics education at the secondary-school level.

7 citationsRead paper
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