Esmaeil S. Nadimi

An Adaptive Path Loss Channel Model for Wave Propagation in Multilayer Transmission Medium

Advances in micro robots in non-invasive medicine have enabled physicians to perform diagnostic and therapeutic procedures with higher resolution and lower risk than before. However, navigation and precise localisation of such micro robots inside human body still remains a challenge. This is mostly due to the 1) lack of precise communication channel models, 2) inhomogeneity of the propagation medium and 3) non-geometric boundaries of the tissues morphometric parameters. In this study, we derive novel intra-body path loss channel models for wave propagation in wireless capsule endoscopy, i.e., propagation through the gastrointestinal tract and the abdominal wall. We formulate an adaptive attenuation parameter as a function of permittivity, conductivity and the thickness of various layers between the transmitter and the receiver. The standard deviation of modelling error of the path loss using our adaptive channel model is smaller than 50% of that of existing channel models. We further analyse the sensitivity of the path loss model to the variations of thickness of different abdominal wall layers. We finally show that the thickness of the fat layer has the greatest influence on the total attenuation parameter of the path loss model and therefore, we modify our adaptive model accordingly.

Bayesian-based localization of wireless capsule endoscope using received signal strength.

In wireless body area sensor networking (WBASN) applications such as gastrointestinal (GI) tract monitoring using wireless video capsule endoscopy (WCE), the performance of out-of-body wireless link propagating through different body media (i.e. blood, fat, muscle and bone) is still under investigation. Most of the localization algorithms are vulnerable to the variations of path-loss coefficient resulting in unreliable location estimation. In this paper, we propose a novel robust probabilistic Bayesian-based approach using received-signal-strength (RSS) measurements that accounts for Rayleigh fading, variable path-loss exponent and uncertainty in location information received from the neighboring nodes and anchors. The results of this study showed that the localization root mean square error of our Bayesian-based method was 1.6 mm which was very close to the optimum Cramer-Rao lower bound (CRLB) and significantly smaller than that of other existing localization approaches (i.e. classical MDS (64.2mm), dwMDS (32.2mm), MLE (36.3mm) and POCS (2.3mm)).

On the ratio of independent complex Gaussian random variables

In this paper, we derive a closed form equation for the joint probability distribution

An adaptive localization technique for wireless capsule endoscopy

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Capsule Endoscopy vs. Colonoscopy vs. Histopathology in Colorectal Cancer Screening: Matched analyses of polyp size, morphology and location estimates

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Texture classification from single uncalibrated images: Random matrix theory approach

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