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Nunzio Tuccitto

Publications and source records attributed to Nunzio Tuccitto.

2 recordsLinked to original sources

Analytical Modeling of Dispersive Closed-loop MC Channels with Pulsatile Flow

Molecular communication (MC) is a communication paradigm in which information is conveyed through the controlled release, propagation, and reception of molecules. Many envisioned healthcare applications of MC are expected to operate inside the human body. In this environment, the cardiovascular system ( CVS) acts as the physical channel, which forms a closed-loop network where particle transport is mainly governed by the combined effects of diffusion and flow. Despite the fact that physiological flows in many parts of the human body are inherently pulsatile due to the cardiac cycle, most existing models for dispersive closed-loop MC channels assume a constant flow velocity. In this paper, we present a time-variant one-dimensional (1D ) channel model for dispersive closed-loop MC systems with pulsatile flow. We derive an analytical expression for the channel impulse response (CIR ), which follows a wrapped Normal distribution with time-variant mean and variance. The obtained model reveals the cyclostationary nature of the channel and quantifies the influence of pulsation on the temporal concentration profile compared to steady-flow systems. Finally, the model is validated by three-dimensional ( 3D ) particle-based simulations (PBS s), showing excellent agreement and enabling an efficient analytical characterization of the channel.

cs.ET↗

Mixture of Inverse Gaussians for Hemodynamic Transport (MIGHT) in Multiple-Input Multiple-Output Vascular Networks

Synthetic molecular communication (MC) in the cardiovascular system is a key enabler for many envisioned medical applications inside the human body, such as targeted drug delivery, early disease detection, and continuous health monitoring. The design of synthetic MC systems for such applications requires suitable models for the signaling molecule propagation through complex vessel networks (VNs). Existing theoretical models offer limited analytical tractability and lack closed-form solutions, making the analysis of realistic large-scale VNs either infeasible or not insightful. To overcome these limitations, in this paper, we propose a novel closed-form physical model, termed mixture of inverse Gaussians for hemodynamic transport (MIGHT), for the advection-diffusion-driven transport of signaling molecules through complex VNs. The model represents the received molecule flux as a weighted sum of inverse Gaussian distributions, parameterized by the physical properties of the underlying VN. We show that MIGHT is capable of accurately representing the transport dynamics of signaling molecules in large-scale VNs ranging from simple single-input single-output (SISO) to complex multiple-input multiple-output (MIMO) network topologies. The accuracy of the proposed model is validated by comparison to the results from an existing convolution-based model and numerical finite-element simulations, with all finite-element simulation data available on Zenodo. Furthermore, we investigate three applications of the model, namely the reduction of SISO-VNs to obtain simplified representations preserving the essential transport dynamics, the identification and analysis of network regions that are most important for molecule transport in MIMO-VNs comprising multiple transmitters and multiple receivers, and the estimation of representative SISO-VNs that can reproduce the received signal of an unknown SISO-VN.

q-bio.QM↗