Muzaffer Kanaan

A Study of the Effects of Reference Point Density on Toa-Based UWB Indoor Positioning Systems

The performance of ultra-wideband (UWB) indoor positioning systems based on time of arrival (TOA) techniques is generally affected by the density of reference points (RPs), as well as undetected direct path (UDP) conditions. For a fixed number of reference points (RPs), the performance of some indoor positioning algorithms tends to degrade as the size of the area is increased, i.e. the RP density is decreased. In this paper, we evaluate the effects of RP density on the performance of different positioning algorithms in the presence of empirical distance measurement error (DME) models derived from UWB measurements in typical indoor environments. We then present functional relationships between RP density and positioning mean-square error (MSE) for these algorithms. These relationships can be used for more effective indoor positioning system design and deployment. Finally, we investigate the effects of bandwidth with respect to improving the performance of these algorithms

Ranging based on maximum likelihood techniques for ultra wideband medical implants

This paper presents a study of ultra wide band (UWB) technology for in-body ranging, specifically geared towards enabling next-generation medical diagnosis, monitoring, imaging and robotic surgery applications. Leveraging UWB radio propagation models for in-body scenarios, we study the performance of Maximum Likelihood (ML) estimation techniques for in-body ranging.

In-body ranging for ultra-wide band wireless capsule endoscopy using a neural network architecture

Accurate localization of the endoscopy capsule can have a significant positive impact on disease diagnosis and treatment using wireless capsule endoscopy. This paper concerns the problem of accurate ranging for localization of an endoscopy capsule. Towards this objective, we propose a range estimator leveraging the use of ultra-wide band signaling and neural networks. Our results indicate the feasibility of obtaining a ranging accuracy in the neighborhood of 21 mm. We also model the statistics of the ranging error, and demonstrate that the error is accurately modeled by a Weibull distribution.

In-Body Ranging with Ultra-Wideband Signals: Techniques and Modeling of the Ranging Error

Results about the problem of accurate ranging within the human body using ultra-wideband signals are shown. The ability to accurately measure the range between a sensor implanted in the human body and an external receiver can make a number of new medical applications such as better wireless capsule endoscopy, next-generation microrobotic surgery systems, and targeted drug delivery systems possible. The contributions of this paper are twofold. First, we propose two novel range estimators: one based on an implementation of the so-called CLEAN algorithm for estimating channel profiles and another based on neural networks. Second, we develop models to describe the statistics of the ranging error for both types of estimators. Such models are important for the design and performance analysis of localization systems. It is shown that the ranging error in both cases follows a heavy-tail distribution known as the Generalized Extreme Value distribution. Our results also indicate that the estimator based on neural networks outperforms the CLEAN-based estimator, providing ranging errors better than or equal to 3.23 mm with 90% probability.

Ranging for in-body localization of ultra wide band wireless endoscopy capsules using neural networks

Wireless capsule endoscopy has come to be regarded as a very important technique for diagnosis of treatment of diseases of the gastrointestinal tract. The wireless capsule endoscopy systems of today do not allow the location of an abnormal condition (tumor or lesion) to be determined. In this work, the problem of localization of an endoscopy capsule within the GI tract, and more specifically the problem of ranging has been treated through the ultra wide band signaling and neural networks.

On the use of ZigBee technology for coal mine safety

Today, coal mines are very dangerous work places and accidents can still occur. In such situations, remote monitoring of the health conditions of miners can better aid search-and-rescue efforts after accidents. In this context, the use of wireless sensor networks can be highly advantageous. In this work, the development of a coal mine safety system based on ZigBee technology is described.

Investigation of shadowing effects in ultra wide band implant Body Area Networks

In this work, An Ultra Wide Band antenna placing at different depths of human body is discussed. Depending on the changes of the bandwidth of the given signal, path loss and shodowing effects outside the body are examined. When calculating the path loss and shadowing effects, three dimensional electromagnetic field software is used. Kolmogorov-Smirnov hypothesis test is subjected to the data obtained from this software. The shadowing effect is detected to be the best compatible with GEV (Generalized Extreme Value) distribution function.

On the relationship between antenna parameters and near-field effects for UWB implant body area networks

We characterize the near-field effects of UWB implant antenna for the implant body area networks in terms of antenna parameters. There has been ongoing investigations about near-field characteristics of an implant UWB antenna regarding antenna geometry. Taking into account the interaction of the implant antenna with body tissues, our observations about optimizing antenna parameters guides this issue that should be useful for improving the quality of an implant UWB link.