Hamideh Ramezani

Fundamentals of Molecular Information and Communication Science

Molecular communication (MC) is the most promising communication paradigm for nanonetwork realization since it is a natural phenomenon observed among living entities with nanoscale components. Since MC significantly differs from classical communication systems, it mandates reinvestigation of information and communication theoretical fundamentals. The closest examples of MC architectures are present inside our own body. Therefore, in this paper, we investigate the existing literature on intrabody nanonetworks and different MC paradigms to establish and introduce the fundamentals of molecular information and communication science. We highlight future research directions and open issues that need to be addressed for revealing the fundamental limits of this science. Although the scope of this development encompasses wide range of applications, we particularly emphasize its significance for life sciences by introducing potential diagnosis and treatment techniques for diseases caused by dysfunction of intrabody nanonetworks.

Synaptic Channel Model Including Effects of Spike Width Variation

An accurate model for neuro-spike communication is important in understanding the fundamentals of molecular communication. However, none of the existing models in the literature studied variations in the shape of action potentials during Axonal propagation, one of the steps during neuro-spike communication. These variations affect the amount of information communicated through a neuron. Hence, analyzing effects of these variations in the release of neurotransmitter, the carrier of information in neuro-spike communication, is imperative in deriving a realistic model for neuro-spike communication. In this work, we improve the existing channel models for synaptic communication to cover the effect of changes in the width of action potential on hippocampal pyramidal neurons based on the experimental data reported in the literature. The receiver neuron is assumed to detect spikes based on Neyman-Pearson method. We derive the structure of this detector for the proposed channel model. Numerical results depict that an increase in the spike width decreases the error probability.

A Communication Theoretical Modeling of Axonal Propagation in Hippocampal Pyramidal Neurons

Understanding the fundamentals of communication among neurons, known as neuro-spike communication, leads to reach bio-inspired nanoscale communication paradigms. In this paper, we focus on a part of neuro-spike communication, known as axonal transmission, and propose a realistic model for it. The shape of the spike during axonal transmission varies according to previously applied stimulations to the neuron, and these variations affect the amount of information communicated between neurons. Hence, to reach an accurate model for neuro-spike communication, the memory of axon and its effect on the axonal transmission should be considered, which are not studied in the existing literature. In this paper, we extract the important factors on the memory of axon and define memory states based on these factors. We also describe the transition among these states and the properties of axonal transmission in each of them. Finally, we demonstrate that the proposed model can follow changes in the axonal functionality properly by simulating the proposed model and reporting the root mean square error between simulation results and experimental data.

Diversity in diffusion-based molecular communication channel with drift

We utilize the well known Additive Inverse Gaussian Noise (AIGN) communication channel to investigate the effect of diversity in diffusion-based molecular communication with drift, where the transmitter releases different types of molecules to the fluid medium by encoding the information onto the release time and type of molecules. The fluid channel imposes extra delay on the communication, and the receiver decodes the encoded information by solely utilizing the molecular arrival times. In this paper, simple receiver models based on maximum likelihood estimation (MLE) are investigated. Furthermore, upper and lower bounds on the capacity of AIGN communication channel with molecular diversity are derived.

Speech features for telemonitoring of Parkinson's disease symptoms

The aim of this paper is tracking Parkinsons disease (PD) progression based on its symptoms on vocal system using Unified Parkinsons Disease Rating Scale (UPDRS). We utilize a standard speech signal feature set, which contains 6373 static features as functionals of low-level descriptor (LLD) contours, and select the most informative ones using the maximal relevance and minimal redundancy based on correlations (mRMRC) criteria. Then, we evaluate performance of Gaussian mixture regression (GMR) and support vector regression (SVR) on estimating the third subscale of UPDRS, i.e., UPDRS: motor subscale (UPDRS-III). Among the most informative features, a list of features are selected after redundancy reduction. The selected features depict that LLDs providing information about spectrum flatness, spectral distribution of energy, and hoarseness of voice are the most important ones for estimating UPDRS-III. Moreover, the most informative statistical functions are related to range, maximum, minimum and standard deviation of LLDs, which is an evidence of the muscle weakness due to the PD. Furthermore, GMR outperforms SVR on compact feature sets while the performance of SVR improves by increasing number of features.