کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5032674 | 1471131 | 2017 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Optimised analytical models of the dielectric properties of biological tissue
ترجمه فارسی عنوان
مدل های تحلیلی بهینه شده از خواص دی الکتریک بافت بیولوژیکی
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کلمات کلیدی
خواص دی الکتریک، بهینه سازی، مدل های پارامتریک، الگوریتم ژنتیک،
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی پزشکی
چکیده انگلیسی
The interaction of electromagnetic fields with the human body is quantified by the dielectric properties of biological tissues. These properties are incorporated into complex numerical simulations using parametric models such as Debye and Cole-Cole, for the computational investigation of electromagnetic wave propagation within the body. These parameters can be acquired through a variety of optimisation algorithms to achieve an accurate fit to measured data sets. A number of different optimisation techniques have been proposed, but these are often limited by the requirement for initial value estimations or by the large overall error (often up to several percentage points). In this work, a novel two-stage genetic algorithm proposed by the authors is applied to optimise the multi-pole Debye parameters for 54 types of human tissues. The performance of the two-stage genetic algorithm has been examined through a comparison with five other existing algorithms. The experimental results demonstrate that the two-stage genetic algorithm produces an accurate fit to a range of experimental data and efficiently out-performs all other optimisation algorithms under consideration. Accurate values of the three-pole Debye models for 54 types of human tissues, over 500Â MHz to 20Â GHz, are also presented for reference.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Medical Engineering & Physics - Volume 43, May 2017, Pages 103-111
Journal: Medical Engineering & Physics - Volume 43, May 2017, Pages 103-111
نویسندگان
Saqib Salahuddin, Emily Porter, Finn Krewer, Martin O' Halloran,