A novel frequency-dependent path loss model for ultra wideband implant body area networks

Kanaan, Muzaffer; Suveren, Memduh

doi:10.1016/j.measurement.2015.02.040

Cited by 11 publications

(13 citation statements)

References 13 publications

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“…The antenna used for the study was the elliptic disk dipole antenna proposed in [23] and used in our previous study [14], as seen in Fig. 3a.…”

Section: Modeling Methodologymentioning

confidence: 99%

“…UWB low band is preferred in this and other studies, owing to previously reported results that signals in this band suffer less attenuation from body tissues [24,25]. The signal power level at the antenna input was set to 1 mW from a 50-X source situated in the feed point between the elliptical disks in the antenna structure [14].…”

Section: Modeling Methodologymentioning

confidence: 99%

“…Previous work by the authors has indicated that a highly nonlinear relationship exists between path loss, body tissue parameters, frequency of operation and the transmitterreceiver separation distance [14]. As such, calculation of the path loss values through the neural network is an Neural Comput & Applic example of approximating a nonlinear function, a task that neural networks are inherently well suited to.…”

Section: Neural Comput and Applicmentioning

confidence: 99%

“…Therefore, Several prior works regarding propagation modeling for implant communications and the UWB case in particular can be found in the literature [10][11][12][13]. To the best of the authors' knowledge, the most recent contribution in this area was again the authors' own, wherein it was attempted to model the frequency-dependent path loss characteristics based on results of finite difference time domain (FDTD) simulations [14]. One drawback of the FDTD approach is that it is very time-consuming, with simulations sometimes taking days or even weeks to complete (subject, as always, to the complexity of the simulation scenario and the computational horsepower available).…”

Section: Introductionmentioning

confidence: 99%

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A new propagation modeling technique for ultra-wideband implant body area networks based on a neural network architecture

Kanaan

Suveren

2016

Neural Comput & Applic

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We report on the results of our investigations into more computationally efficient methods for propagation modeling in the implant environment through the use of neural networks. Our results are applicable to the important case of ultra-wideband systems operating in the implant environment. A propagation modeling engine based on neural networks can help the system designer avoid time-consuming numerical simulations normally utilized for propagation modeling. The contributions of this paper are as follows. We perform a systematic investigation of two neural network architectures for propagation modeling, namely the multilayer perceptron (MLP) and the recurrent Elman network. We perform a quantitative comparison of the results produced by these networks to the results produced by time-consuming numerical methods, such as the finite difference time domain (FDTD) method. Our results indicate that it is indeed possible to obtain path loss results using neural network methods that approach FDTD results within a 3-to 4-dB margin of error. We further illustrate that it is possible to attain this level of accuracy through the simpler MLP structure, and that the increased complexity and training time associated with the Elman network is unwarranted. We also observe that the MLP network can attain this performance through reasonable network sizes of 20-30 neurons.

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“…The antenna used for the study was the elliptic disk dipole antenna proposed in [23] and used in our previous study [14], as seen in Fig. 3a.…”

Section: Modeling Methodologymentioning

confidence: 99%

Section: Modeling Methodologymentioning

confidence: 99%

Section: Neural Comput and Applicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new propagation modeling technique for ultra-wideband implant body area networks based on a neural network architecture

Kanaan

Suveren

2016

Neural Comput & Applic

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“…Therefore, a new type of capsule endoscopy with ultra-wideband (UWB) [25][26][27][28] radar is utilized in this study for the first time. The medical imaging problem is a special case of electromagnetic inverse scattering problem.…”

Section: Introductionmentioning

confidence: 99%

Stomach Tumor Localization Method of a Support Vector Machine Based on Capsule Endoscopy

Gong¹,

Zhang²

2017

PIER B

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Abstract-This study proposes a real-time method to solve the electromagnetic inverse scattering problem. This technique converts this problem into a regression problem using a support vector machine (SVM). The SVM-based solution successfully deals with the nonlinearity and ill-posedness inherent in this problem. Simulation results show the feasibility and effectiveness of the proposed method. The method can effectively locate the tumor target of the stomach regardless of the presence of noise. The positioning effect of the method improves as SNR increases. When the SNR is higher than 50 dB, noise minimally affects the results. Finally, the SVM prediction model is utilized to study the effect of the number of sampling locations on the prediction results. The results show that the more sampling locations, the better the prediction results.

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Experimental UWB indoor channel characterization in stationary and mobility scheme

et al. 2017

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A novel frequency-dependent path loss model for ultra wideband implant body area networks

Cited by 11 publications

References 13 publications

A new propagation modeling technique for ultra-wideband implant body area networks based on a neural network architecture

A new propagation modeling technique for ultra-wideband implant body area networks based on a neural network architecture

Stomach Tumor Localization Method of a Support Vector Machine Based on Capsule Endoscopy

Experimental UWB indoor channel characterization in stationary and mobility scheme

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