890 research outputs found
Modeling convection-diffusion-reaction systems for microfluidic molecular communications with surface-based receivers in Internet of Bio-Nano Things.
We consider a microfluidic molecular communication (MC) system, where the concentration-encoded molecular messages are transported via fluid flow-induced convection and diffusion, and detected by a surface-based MC receiver with ligand receptors placed at the bottom of the microfluidic channel. The overall system is a convection-diffusion-reaction system that can only be solved by numerical methods, e.g., finite element analysis (FEA). However, analytical models are key for the information and communication technology (ICT), as they enable an optimisation framework to develop advanced communication techniques, such as optimum detection methods and reliable transmission schemes. In this direction, we develop an analytical model to approximate the expected time course of bound receptor concentration, i.e., the received signal used to decode the transmitted messages. The model obviates the need for computationally expensive numerical methods by capturing the nonlinearities caused by laminar flow resulting in parabolic velocity profile, and finite number of ligand receptors leading to receiver saturation. The model also captures the effects of reactive surface depletion layer resulting from the mass transport limitations and moving reaction boundary originated from the passage of finite-duration molecular concentration pulse over the receiver surface. Based on the proposed model, we derive closed form analytical expressions that approximate the received pulse width, pulse delay and pulse amplitude, which can be used to optimize the system from an ICT perspective. We evaluate the accuracy of the proposed model by comparing model-based analytical results to the numerical results obtained by solving the exact system model with COMSOL Multiphysics
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Maximum Likelihood Detection With Ligand Receptors for Diffusion-Based Molecular Communications in Internet of Bio-Nano Things.
Molecular Communication (MC) is a bio-inspired communication technique that uses molecules as a method of information transfer among nanoscale devices. MC receiver is an essential component having profound impact on the communication system performance. However, the interaction of the receiver with information bearing molecules has been usually oversimplified in modeling the reception process and developing signal detection techniques. In this paper, we focus on the signal detection problem of MC receivers employing receptor molecules to infer the transmitted messages encoded into the concentration of molecules, i.e., ligands. Exploiting the observable characteristics of ligand-receptor binding reaction, we first introduce a Maximum Likelihood (ML) detection method based on instantaneous receptor occupation ratio, as aligned with the current MC literature. Then, we propose a novel ML detection technique, which exploits the amount of time the receptors stay unbound in an observation time window. A comprehensive analysis is carried out to compare the performance of the detectors in terms of bit error probability. In evaluating the detection performance, emphasis is given to the receptor saturation problem resulting from the accumulation of messenger molecules at the receiver as a consequence of intersymbol interference. The results reveal that detection based on receptor unbound time is quite reliable even in saturation, whereas the reliability of detection based on receptor occupation ratio substantially decreases as the receiver gets saturated. Finally, we also discuss the potential methods of implementing the detectors
Odor Intensity Shift Keying (OISK) and Channel Capacity of Odor-based Molecular Communications in Internet of Everything
Molecular communication is a new, active area of research that has created a
paradigm shift in the way a communication system is perceived. An artificial
molecular communication network is created using biological molecules for
encoding, transmitting and decoding the symbols to convey information. In
addition to typical biological molecules, we are also exploring other classes
of molecules that possess unique distinctive features which can be potentially
exploited for establishing reliable communications. Odor molecules are one such
class of molecules which possess several distinctive features such as
Intensity, Headonic tone which provides a basis to convey the information in an
olfactory communication system. In our work, we investigate the ICT
(information and communication theory) perspective of the olfactory
communications by evaluating the channel capacity of an odor molecular
communication (OMC) system with the help of a novel modulation scheme viz. odor
intensity shift keying (OISK), where information is being conveyed from the
intensity level of an odor. Furthermore, we also analyse the effects of
critical parameters like temperature and noise on the achievable channel
capacity to provide an insight about the resilience of the proposed OMC system
towards any such anomaly faced by it
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Impacts of Spike Shape Variations on Synaptic Communication.
Understanding the communication theoretical capabilities of information transmission among neurons, known as neuro-spike communication, is a significant step in developing bio-inspired solutions for nanonetworking. In this paper, we focus on a part of this communication known as synaptic transmission for pyramidal neurons in the Cornu Ammonis area of the hippocampus location in the brain and propose a communication-based model for it that includes effects of spike shape variation on neural calcium signaling and the vesicle release process downstream of it. For this aim, we find impacts of spike shape variation on opening of voltage-dependent calcium channels, which control the release of vesicles from the pre-synaptic neuron by changing the influx of calcium ions. Moreover, we derive the structure of the optimum receiver based on the Neyman-Pearson detection method to find the effects of spike shape variations on the functionality of neuro-spike communication. Numerical results depict that changes in both spike width and amplitude affect the error detection probability. Moreover, these two factors do not control the performance of the system independently. Hence, a proper model for neuro-spike communication should contain effects of spike shape variations during axonal transmission on both synaptic propagation and spike generation mechanisms to enable us to accurately explain the performance of this communication paradigm
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Importance of vesicle release stochasticity in neuro-spike communication.
Aim of this paper is proposing a stochastic model for vesicle release process, a part of neuro-spike communication. Hence, we study biological events occurring in this process and use microphysiological simulations to observe functionality of these events. Since the most important source of variability in vesicle release probability is opening of voltage dependent calcium channels (VDCCs) followed by influx of calcium ions through these channels, we propose a stochastic model for this event, while using a deterministic model for other variability sources. To capture the stochasticity of calcium influx to pre-synaptic neuron in our model, we study its statistics and find that it can be modeled by a distribution defined based on Normal and Logistic distributions.This work was supported in part by ERC project MINERVA (ERC-2013- CoG #616922), EU project CIRCLE (EU-H2020-FET-Open #665564), and TU˘ BI˙TAK graduate scholarship program (BIDEB-2215). 1MCell development is supported by the NIGMS-funded (P41GM103712) National Center for Multiscale Modeling of Biological Systems (MMBioS)
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