scholarly journals An Efficient Algorithm for EM Scattering from Anatomically Realistic Human Head Model Using Parallel CG-FFT Method

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Lei Zhao ◽  
Gen Chen

An efficient algorithm is proposed to analyze the electromagnetic scattering problem from a high resolution head model with pixel data format. The algorithm is based on parallel technique and the conjugate gradient (CG) method combined with the fast Fourier transform (FFT). Using the parallel CG-FFT method, the proposed algorithm is very efficient and can solve very electrically large-scale problems which cannot be solved using the conventional CG-FFT method in a personal computer. The accuracy of the proposed algorithm is verified by comparing numerical results with analytical Mie-series solutions for dielectric spheres. Numerical experiments have demonstrated that the proposed method has good performance on parallel efficiency.

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4061 ◽  
Author(s):  
Awais Munawar Qureshi ◽  
Zartasha Mustansar

In this paper, we have presented a microwave scattering analysis from multiple human head models. This study incorporates different levels of detail in the human head models and its effect on microwave scattering phenomenon. Two levels of detail are taken into account; (i) Simplified ellipse shaped head model (ii) Anatomically realistic head model, implemented using 2-D geometry. In addition, heterogenic and frequency-dispersive behavior of the brain tissues has also been incorporated in our head models. It is identified during this study that the microwave scattering phenomenon changes significantly once the complexity of head model is increased by incorporating more details using magnetic resonance imaging database. It is also found out that the microwave scattering results match in both types of head model (i.e., geometrically simple and anatomically realistic), once the measurements are made in the structurally simplified regions. However, the results diverge considerably in the complex areas of brain due to the arbitrary shape interface of tissue layers in the anatomically realistic head model.After incorporating various levels of detail, the solution of subject microwave scattering problem and the measurement of transmitted and backscattered signals were obtained using finite element method. Mesh convergence analysis was also performed to achieve error free results with a minimum number of mesh elements and a lesser degree of freedom in the fast computational time. The results were promising and the E-Field values converged for both simple and complex geometrical models. However, the E-Field difference between both types of head model at the same reference point differentiated a lot in terms of magnitude. At complex location, a high difference value of 0.04236 V/m was measured compared to the simple location, where it turned out to be 0.00197 V/m. This study also contributes to provide a comparison analysis between the direct and iterative solvers so as to find out the solution of subject microwave scattering problem in a minimum computational time along with memory resources requirement.It is seen from this study that the microwave imaging may effectively be utilized for the detection, localization and differentiation of different types of brain stroke. The simulation results verified that the microwave imaging can be efficiently exploited to study the significant contrast between electric field values of the normal and abnormal brain tissues for the investigation of brain anomalies. In the end, a specific absorption rate analysis was carried out to compare the ionizing effects of microwave signals to different types of head model using a factor of safety for brain tissues. It is also suggested after careful study of various inversion methods in practice for microwave head imaging, that the contrast source inversion method may be more suitable and computationally efficient for such problems.


2019 ◽  
Vol 8 (2) ◽  
pp. 53-58
Author(s):  
E. Konakyeri Arıcı ◽  
A. Yapar

In this study, an inverse scattering approach is investigated for the detection and imaging of an abnormal structure (a bleeding or a stroke) inside the human brain. The method is mainly based on the solution of an integral equation whose kernel is the Green’s function of the inhomogeneous medium (corresponding to a human head model) which is obtained by a numerical approach based on Method of Moments (MoM). In this context, an inverse scattering problem related to the difference of healthy and unhealthy brain models is formulated and a difference function is obtained which indicates the region where the anomaly is located by solving this inverse problem. In order to reduce the reflection effects caused by the electromagnetic differences between the free space and the brain, a matching medium is used as the background space.


Frequenz ◽  
2016 ◽  
Vol 70 (1-2) ◽  
Author(s):  
Chang-Ze Li ◽  
Chuangming Tong ◽  
Lihui Qi ◽  
Weijie Wang ◽  
An Wang

AbstractAn iterative physical optics (IPO) is proposed to solve the extra large scale (e.g. larger than one thousand square lambda) electromagnetic (EM) scattering from randomly rough surfaces in this paper. The forward-backward methodology and its modification with under-relaxation iteration improve convergence and stability of the IPO; the fast far-field approximation (FaFFA) in the matrix-vector product reduces the computational complexity based on the scattering characteristics of rough surface. Through these techniques, this model can solve effectively the extra large scale scattering problem from the randomly rough surfaces.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Alireza Chamanzar ◽  
Marlene Behrmann ◽  
Pulkit Grover

AbstractA rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.


2014 ◽  
Vol 26 (4) ◽  
pp. 781-817 ◽  
Author(s):  
Ching-Pei Lee ◽  
Chih-Jen Lin

Linear rankSVM is one of the widely used methods for learning to rank. Although its performance may be inferior to nonlinear methods such as kernel rankSVM and gradient boosting decision trees, linear rankSVM is useful to quickly produce a baseline model. Furthermore, following its recent development for classification, linear rankSVM may give competitive performance for large and sparse data. A great deal of works have studied linear rankSVM. The focus is on the computational efficiency when the number of preference pairs is large. In this letter, we systematically study existing works, discuss their advantages and disadvantages, and propose an efficient algorithm. We discuss different implementation issues and extensions with detailed experiments. Finally, we develop a robust linear rankSVM tool for public use.


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