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Inaki Esnaola
Researcher at University of Sheffield
Publications - 81
Citations - 1823
Inaki Esnaola is an academic researcher from University of Sheffield. The author has contributed to research in topics: Computer science & Compressed sensing. The author has an hindex of 13, co-authored 71 publications receiving 1503 citations. Previous affiliations of Inaki Esnaola include University of Navarra & Bell Labs.
Papers
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Power Allocation Strategies in Energy Harvesting Wireless Cooperative Networks
TL;DR: The focus of this paper is on the relay's strategies to distribute the harvested energy among the multiple users and their impact on the system performance, and asymptotic results show that its outage performance decays as log SNR/SNR.
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Machine Learning Methods for Attack Detection in the Smart Grid
TL;DR: Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.
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Sparse Attack Construction and State Estimation in the Smart Grid: Centralized and Distributed Models
TL;DR: Novel formulations for the optimization problem that provide a flexible design of the trade-off between performance and false alarm are proposed and the centralized case is extended to a distributed framework for both the estimation and attack problems.
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Sparse Attack Construction and State Estimation in the Smart Grid: Centralized and Distributed Models
TL;DR: In this paper, new methods that exploit sparse structures arising in smart grid networks are proposed for the state estimation problem when data injection attacks are present, which provide a flexible design of the trade-off between performance and false alarm.
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SPADEDH: a sparsity-based denoising method of digital holograms without knowing the noise statistics
Pasquale Memmolo,Inaki Esnaola,Andrea Finizio,Melania Paturzo,Pietro Ferraro,Antonia M. Tulino +5 more
TL;DR: This paper proposes a robust method to suppress the noise components in digital holography (DH), called SPADEDH (SPArsity DEnoising of Digital Holograms), that does not consider any prior knowledge or estimation about the statistics of the noise.