Nolasco Ferencikova, C. (Carolina)

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    Quantum Error Correction for Realistic Decoherence Models.
    (Servicio de Publicaciones. Universidad de Navarra., 2022-10-07) Nolasco Ferencikova, C. (Carolina)
    Quantum Computing has garnered the attention of the scientific community, due to its revolutionary potential to tackle problems that are infeasible with classical computation. Quantum Error Correction is the field of study in quantum communication that focuses on detecting and correcting errors, caused by decoherence, that corrupt quantum information, in order to make fault-tolerant Quantum Computing feasible. Quantum channels model the effects of decoherence, which, in recent studies, have been proven to be time-variant. Current correction schemes, which were designed considering static decoherence models, under-perform under time-varying noise scenarios. In order to adapt these schemes, it is necessary to correctly estimate the channel error and detect its time variations. With this framework, the goal of this project is to study how to estimate quantum channel error probabilities with its time fluctuations effectively and with minimum delay.
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    Distributed clustering algorithm for adaptive pandemic control.
    (IEEE, 2021) Zárraga-Rodríguez, M. (Marta de); Insausti-Sarasola, X. (Xabier); Gutiérrez-Gutiérrez, J. (Jesús); Nolasco Ferencikova, C. (Carolina)
    The COVID-19 pandemic has had severe consequences on the global economy, mainly due to indiscriminate geographical lockdowns. Moreover, the digital tracking tools developed to survey the spread of the virus have generated serious privacy concerns. In this paper, we present an algorithm that adaptively groups individuals according to their social contacts and their risk level of severe illness from COVID-19, instead of geographical criteria. The algorithm is fully distributed and therefore, individuals do not know any information about the group they belong to. Thus, we present a distributed clustering algorithm for adaptive pandemic control.
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    In-network algorithm for passive sensors in structural health monitoring
    (2023) Zárraga-Rodríguez, M. (Marta de); Insausti-Sarasola, X. (Xabier); Gutiérrez-Gutiérrez, J. (Jesús); Nolasco Ferencikova, C. (Carolina)
    Structural health monitoring (SHM) using wireless sensor networks (WSN) has become a popular implementation, due to low maintenance and installation costs. These networks commonly use a centralized approach and battery-powered sensors, leading to energy consumption limitations, in both the central unit and the sensors. Therefore, it is of interest to consider the use of passive sensors and distributed processing in the network. In this letter, we present a distributed algorithm for SHM using wireless passive sensor networks (WPSNs) that allows any passive sensor in the network to obtain the distance to its neighbours via backscattering, and hence to detect and signal changes in the monitored structure.