arXiv subjects

Fatih Gulec

Publications and source records attributed to Fatih Gulec.

13 recordsLinked to original sources

arXiv2026

Universal Costas Matrices: Towards a General Framework for Costas Array Construction

Costas arrays are a special type of permutation matrices with ideal autocorrelation and low cross-correlation properties, making them valuable for radar, wireless communication, and integrated sensing and communication applications. This paper presents a novel unified framework for analyzing and discovering new Costas arrays. We introduce Universal Costas Matrices (UCMs) and Universal Costas Frequency Matrices (UCFMs) and investigate their structural characteristics. A framework integrating UCMs and UCFMs is proposed to pave the way for future artificial intelligence-assisted Costas array discovery. Leveraging the structural properties of UCMs and UCFMs, a reconstruction-based search method is developed to generate UCMs from UCFMs. Numerical results demonstrate that the proposed approach significantly accelerates the search process and enhances structural insight into Costas array generation.

cs.IT↗

arXiv2025

Empowering Nanoscale Connectivity through Molecular Communication: A Case Study of Virus Infection

The Internet of Bio-Nano Things (IoBNT), envisioned as a revolutionary healthcare paradigm, shows promise for epidemic control. This paper explores the potential of using molecular communication (MC) to address the challenges in constructing IoBNT for epidemic prevention, specifically focusing on modeling viral transmission, detecting the virus/infected individuals, and identifying virus mutations. First, the MC channels in macroscale and microscale scenarios are discussed to match viral transmission in both scales separately. Besides, the detection methods for these two scales are also studied, along with the localization mechanism designed for the virus/infected individuals. Moreover, an identification strategy is proposed to determine potential virus mutations, which is validated through simulation using the ORF3a protein as a benchmark. Finally, open research issues are discussed. In summary, this paper aims to analyze viral transmission through MC and combat viral spread using signal processing techniques within MC.

cs.NI↗

arXiv2025

Decoding and Engineering the Phytobiome Communication for Smart Agriculture

Smart agriculture applications, integrating technologies like the Internet of Things and machine learning/artificial intelligence (ML/AI) into agriculture, hold promise to address modern challenges of rising food demand, environmental pollution, and water scarcity. Alongside the concept of the phytobiome, which defines the area including the plant, its environment, and associated organisms, and the recent emergence of molecular communication (MC), there exists an important opportunity to advance agricultural science and practice using communication theory. In this article, we motivate to use the communication engineering perspective for developing a holistic understanding of the phytobiome communication and bridge the gap between the phytobiome communication and smart agriculture. Firstly, an overview of phytobiome communication via molecular and electrophysiological signals is presented and a multi-scale framework modeling the phytobiome as a communication network is conceptualized. Then, how this framework is used to model electrophysiological signals is demonstrated with plant experiments. Furthermore, possible smart agriculture applications, such as smart irrigation and targeted delivery of agrochemicals, through engineering the phytobiome communication are proposed. These applications merge ML/AI methods with the Internet of Bio-Nano-Things enabled by MC and pave the way towards more efficient, sustainable, and eco-friendly agricultural production. Finally, the implementation challenges, open research issues, and industrial outlook for these applications are discussed.

eess.SP↗

arXiv2023

Localization of a Passive Source with a Sensor Network based Experimental Molecular Communication Platform

In a practical molecular communication scenario such as monitoring air pollutants released from an unknown source, it is essential to estimate the location of the molecular transmitter (TX). This paper presents a novel Sensor Network-based Localization Algorithm (SNCLA) for passive transmission by using a novel experimental platform which mainly comprises a clustered sensor network (SN) with $24$ sensor nodes and evaporating ethanol molecules as the passive TX. In SNCLA, a Gaussian plume model is employed to derive the location estimator. The parameters such as transmitted mass, wind velocity, detection time, and actual concentration are calculated or estimated from the measured signals via the SN to be employed as the input for the location estimator. The numerical results show that the performance of SNCLA is better for stronger winds in the medium. Our findings show that evaporated molecules do not propagate homogeneously through the SN due to the presence of the wind. In addition, our statistical analysis based on the measured experimental data shows that the sensed signals by the SN have a log-normal distribution, while the additive noise follows a Student's t-distribution in contrast to the Gaussian assumption in the literature.

eess.SP↗

arXiv2023

A Computational Approach for the Characterization of Airborne Pathogen Transmission in Turbulent Molecular Communication Channels

Airborne pathogen transmission mechanisms play a key role in the spread of infectious diseases such as COVID-19. In this work, we propose a computational fluid dynamics (CFD) approach to model and statistically characterize airborne pathogen transmission via pathogen-laden particles in turbulent channels from a molecular communication viewpoint. To this end, turbulent flows induced by coughing and the turbulent dispersion of droplets and aerosols are modeled by using the Reynolds-averaged Navier-Stokes equations coupled with the realizable $k-\epsilon$ model and the discrete random walk model, respectively. Via simulations realized by a CFD simulator, statistical data for the number of received particles are obtained. These data are post-processed to obtain the statistical characterization of the turbulent effect in the reception and to derive the probability of infection. Our results reveal that the turbulence has an irregular effect on the probability of infection, which shows itself by the multi-modal distribution as a weighted sum of normal and Weibull distributions. Furthermore, it is shown that the turbulent MC channel is characterized via multi-modal, i.e., sum of weighted normal distributions, or stable distributions, depending on the air velocity.

physics.flu-dyn↗

arXiv2023

A Stochastic Biofilm Disruption Model based on Quorum Sensing Mimickers

Quorum sensing (QS) mimickers can be used as an effective tool to disrupt biofilms which consist of communicating bacteria and extracellular polymeric substances. In this paper, a stochastic biofilm disruption model based on the usage of QS mimickers is proposed. A chemical reaction network (CRN) involving four different states is employed to model the biological processes during the biofilm formation and its disruption via QS mimickers. In addition, a state-based stochastic simulation algorithm is proposed to simulate this CRN. The proposed model is validated by the in vitro experimental results of Pseudomonas aeruginosa biofilm and its disruption by rosmarinic acid as the QS mimicker. Our results show that there is an uncertainty in state transitions due to the effect of the randomness in the CRN. In addition to the QS activation threshold, the presented work demonstrates that there are underlying two more thresholds for the disruption of EPS and bacteria, which provides a realistic modeling for biofilm disruption with QS mimickers.

q-bio.QM↗

arXiv2022

Stochastic Modeling of Biofilm Formation with Bacterial Quorum Sensing

Bacteria generally live in complicated structures called biofilms, consisting of communicating bacterial colonies and extracellular polymeric substance (EPS). Since biofilms are related to detrimental effects such as infection or antibiotic resistance in different settings, it is essential to model their formation. In this paper, a stochastic model is proposed for biofilm formation, using bacterial quorum sensing (QS). In this model, the biological processes in the biofilm formation are modeled as a chemical reaction network which includes bacterial reproduction, productions of autoinducer and EPS, and their diffusion. The modified explicit tau-leap simulation algorithm is adapted based on the two-state QS mechanism. Our approach is validated by using the experimental results of $\textit{Pseudomonas putida}$ IsoF bacteria for autoinducer and bacteria concentration. It is also shown that the percentage of EPS in the biofilm increases significantly after the state change in QS, while it decreases before QS is activated. The presented work shows how the biofilm growth can be modeled realistically by using the QS mechanism in stochastic simulations of chemical reactions.

q-bio.QM↗

arXiv2020

Mobile Human Ad Hoc Networks: A Communication Engineering Viewpoint on Interhuman Airborne Pathogen Transmission

A number of transmission models for airborne pathogens transmission, as required to understand airborne infectious diseases such as COVID-19, have been proposed independently from each other, at different scales, and by researchers from various disciplines. We propose a communication engineering approach that blends different disciplines such as epidemiology, biology, medicine, and fluid dynamics. The aim is to present a unified framework using communication engineering, and to highlight future research directions for modeling the spread of infectious diseases through airborne transmission. We introduce the concept of mobile human ad hoc networks (MoHANETs), which exploits the similarity of airborne transmission-driven human groups with mobile ad hoc networks and uses molecular communication as the enabling paradigm. In the MoHANET architecture, a layered structure is employed where the infectious human emitting pathogen-laden droplets and the exposed human to these droplets are considered as the transmitter and receiver, respectively. Our proof-of-concept results, which we validated using empirical COVID-19 data, clearly demonstrate the ability of our MoHANET architecture to predict the dynamics of infectious diseases by considering the propagation of pathogen-laden droplets, their reception and mobility of humans.

eess.SP↗

arXiv2020

A Molecular Communication Perspective on Airborne Pathogen Transmission and Reception via Droplets Generated by Coughing and Sneezing

Infectious diseases spread via pathogens such as viruses and bacteria. Airborne pathogen transmission via droplets is an important mode for infectious diseases. In this paper, the spreading mechanism of infectious diseases by airborne pathogen transmission between two humans is modeled with a molecular communication perspective. An end-to-end system model which considers the pathogen-laden cough/sneeze droplets as the input and the infection state of the human as the output is proposed. This model uses the gravity, initial velocity and buoyancy for the propagation of droplets and a receiver model which considers the central part of the human face as the reception interface is proposed. Furthermore, the probability of infection for an uninfected human is derived by modeling the number of propagating droplets as a random process. The numerical results reveal that exposure time and sex of the human affect the probability of infection. In addition, the social distance for a horizontal cough should be at least 1.7 m and the safe coughing angle of a coughing human to infect less people should be less than -25$^\circ$.

eess.SP↗

arXiv2020

A Fluid Dynamics Approach to Channel Modeling in Macroscale Molecular Communication

In this paper, a novel fluid dynamics-based approach to channel modeling, which considers liquid droplets as information carriers instead of molecules in the molecular communication (MC) channel, is proposed for practical macroscale MC systems. This approach considers a two-phase flow which is generated by the interaction of droplets in liquid phase with air molecules in gas phase. Two-phase flow is combined with the signal reconstruction (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.

eess.SP↗

arXiv2020

Fluid Dynamics-Based Distance Estimation Algorithm for Macroscale Molecular Communication

Many species, from single-cell bacteria to advanced animals, use molecular communication (MC) to share information with each other via chemical signals. Although MC is mostly studied in microscale, new practical applications emerge in macroscale. It is essential to derive an estimation method for channel parameters such as distance for practical macroscale MC systems which include a sprayer emitting molecules as a transmitter (TX) and a sensor as the receiver (RX). In this paper, a novel approach based on fluid dynamics is proposed for the derivation of the distance estimation in practical MC systems. According to this approach, transmitted molecules are considered as moving droplets in the MC channel. With this approach, the Fluid Dynamics-Based Distance Estimation (FDDE) algorithm which predicts the propagation distance of the transmitted droplets by updating the diameter of evaporating droplets at each time step is proposed. FDDE algorithm is validated by experimental data. The results reveal that the distance can be estimated by the fluid dynamics approach which introduces novel parameters such as the volume fraction of droplets in a mixture of air and liquid droplets and the beamwidth of the TX. Furthermore, the effect of the evaporation is shown with the numerical results.

physics.flu-dyn↗

arXiv2019

Distance Estimation Methods for a Practical Macroscale Molecular Communication System

Accurate estimation of the distance between the transmitter (TX) and the receiver (RX) in molecular communication (MC) systems can provide faster and more reliable communication. Existing theoretical models in the literature are not suitable for distance estimation in a practical scenario. Furthermore, deriving an analytical model is not easy due to effects such as boundary conditions in the diffusion process, the initial velocity of the molecules and unsteady flows. Therefore, five different practical methods comprising three novel data analysis based methods and two supervised machine learning (ML) methods, Multivariate Linear Regression (MLR) and Neural Network Regression (NNR), are proposed for distance estimation at the RX side. In order to apply the ML methods, a macroscale practical MC system, which consists of an electric sprayer without a fan, alcohol molecules, an alcohol sensor and a microcontroller, is established, and the received signals are recorded. A feature extraction algorithm is proposed to utilize the measured signals as the inputs in ML methods. The numerical results show that the ML methods outperform the data analysis based methods in the root mean square error sense with the cost of complexity. Moreover, the peak time based estimation, which is one of the proposed data analysis based methods, yields better results with respect to the other proposed four methods, as the distance increases. Given the experimental data and fluid dynamics theory, a possible trajectory of the molecules between the TX and RX is given. Our findings show that distance estimation performance is jointly affected by unsteady flows and the non-linearity of the sensor. According to our findings based on fluid dynamics, it is evaluated that fluid dynamics should be taken into account for more accurate parameter estimation in practical macroscale MC systems.

eess.SP↗

arXiv2019

Signal reconstruction in diffusion-based molecular communication

Molecular Communication (MC) is an important nanoscale communication paradigm, which is employed for the interconnection of the nanomachines (NMs) to form nanonetworks. A transmitter NM (TN) sends the information symbols by emitting molecules into the transmission medium and a receiver NM (RN) receives the information symbols by sensing the molecule concentration. In this paper, a model of how a RN measures and reconstructs the molecular signal is proposed. The signal around the RN is assumed to be a Gaussian random process instead of deterministic approach in a more realistic way. After the reconstructed signal is derived as a Doubly Stochastic Poisson Process (DSPP), the distortion between the signal around the RN and the reconstructed signal is derived as a new performance parameter in MC systems. The derived distortion which is a function of system parameters such as RN radius, sampling period and the diffusion coefficient of the channel, is shown to be valid by employing random walk simulations. Then, it is shown that the original signal can be satisfactorily reconstructed with a sufficiently low-level of distortion. Finally, optimum RN design parameters, namely RN radius, sampling period and sampling frequency, are derived by minimizing the signal distortion. The simulation results reveal that there is a trade-off among the RN design parameters which can be jointly set for a desired signal distortion.

eess.SP↗