Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/11

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Subject: search.filters.subject.Signal reconstruction

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  • Article
    Citation - WoS: 14
    Citation - Scopus: 15
    A Droplet-Based Signal Reconstruction Approach To Channel Modeling in Molecular Communication
    (Institute of Electrical and Electronics Engineers Inc., 2021) Güleç, Fatih; Güleç, Fatih; Atakan, Barış; Atakan, Barış; 01.01. Units Affiliated to the Rectorate; 03.05. Department of Electrical and Electronics Engineering; 01. Izmir Institute of Technology; 03. Faculty of Engineering
    In this paper, a novel droplet-based signal reconstruction (SR) approach to channel modeling, which considers liquid droplets as information carriers instead of molecules in the molecular communication (MC) channel, is proposed for practical sprayer-based macroscale MC systems. These practical MC systems are significant, since they can be used in order to investigate airborne pathogen transmission with biological sensors due to the similar mechanisms of sneezing/coughing and sprayer. Our proposed approach takes a two-phase flow which is generated by the interaction of droplets in liquid phase with air molecules in gas phase into account. Two-phase flow is combined with the SR of the receiver (RX) to propose a channel model. The SR part of the model quantifies how the accuracy of the sensed molecular signal in its reception volume depends on the sensitivity response of the RX and the adhesion/detachment process of droplets. The proposed channel model is validated by employing experimental data. IEEE
  • Article
    Citation - WoS: 2
    Citation - Scopus: 4
    Constraint Removal for Sparse Signal Recovery
    (Elsevier Ltd., 2012) Şahin, Ahmet; Özen, Serdar; Özen, Serdar; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    This paper presents a new iterative algorithm called constraint removal (CR) for the recovery of a sparse signal x from an incomplete number of linear measurements y such that ym× 1= Am× nxn× 1 and m<n. It is empirically demonstrated that the CR algorithm has a recovery performance which is between basis pursuit linear programming (BP-LP) and subspace pursuit (SP) for both zero-one and Gaussian type signals.