• Title/Summary/Keyword: Microwave path loss modeling

Search Result 3, Processing Time 0.017 seconds

Statistic Microwave Path Loss Modeling in Urban Line-of-Sight Area Using Fuzzy Linear Regression

  • Phaiboon, Supachai;Phokharatkul, Pisit;Somkurnpanit, Suripon
    • 제어로봇시스템학회:학술대회논문집
    • /
    • 2005.06a
    • /
    • pp.1249-1253
    • /
    • 2005
  • This paper presents a method to model the path loss characteristics in microwave urban line-of-sight (LOS) propagation. We propose new upper- and lower-bound models for the LOS path loss using fuzzy linear regression (FLR). The spread of upper- and lower-bound of FLR depends on max and min value of a sample path loss data while the conventional upper- and lower-bound models, the spread of the bound intervals are fixed and do not depend on the sample path loss data. Comparison of our models to conventional upper- and lower-bound models indicate that improvements in accuracy over the conventional models are achieved.

  • PDF

Development of Effective Analytical Signal Models for Functional Microwave Imaging

  • Baang, Sung-Keun;Kim, Jong-Dae;Lee, Yong-Up;Park, Chan-Young
    • Journal of Biomedical Engineering Research
    • /
    • v.28 no.4
    • /
    • pp.471-476
    • /
    • 2007
  • Various active microwave imaging techniques have been developed for cancer detection for past several decades. Both the microwave tomography and the UWB radar techniques, constituting functional microwave imaging systems, use the electrical property contrast between normal tissues and malignancies to detect the latter in an early development stage. Even though promising simulation results have been reported, the understanding of the functional microwave imaging diagnostics has been relied heavily on the complicated numerical results. We present a computationally efficient and physically instructive analytical electromagnetic wave channel models developed for functional microwave imaging system in order to detect especially the breast tumors as early as possible. The channel model covers the propagation factors that have been examined in the previous 2-D models, such as the radial spreading, path loss, partial reflection and transmission of the backscattered electromagnetic waves from the tumor cell. The effects of the system noise and the noise from the inhomogeneity of the tissue to the reconstruction algorithm are modeled as well. The characteristics of the reconstructed images of the tumor using the proposed model are compared with those from the confocal microwave imaging.

Impact of the human body in wireless propagation of medical implants for tumor detection

  • Morocho-Cayamcela, Manuel Eugenio;Kim, Myung-Sik;Lim, Wansu
    • Journal of Internet Computing and Services
    • /
    • v.21 no.2
    • /
    • pp.19-26
    • /
    • 2020
  • This paper analyses the feasibility of using implantable antennas to detect and monitor tumors. We analyze this setting according to the wireless propagation loss and signal fading produced by human bodies and their environment in an indoor scenario. The study is based on the ITU-R propagation recommendations and prediction models for the planning of indoor radio communication systems and radio local area networks in the frequency range of 300 MHz to 100 GHz. We conduct primary estimations on 915 MHz and 2.4 GHz operating frequencies. The path loss presented in most short-range wireless implant devices does not take into account the human body as a channel itself, which causes additional losses to wireless designs. In this paper, we examine the propagation through the human body, including losses taken from bones, muscles, fat, and clothes, which results in a more accurate characterization and estimation of the channel. The results obtained from our simulation indicates a variation of the return loss of the spiral antenna when a tumor is located near the implant. This knowledge can be applied in medical detection, and monitoring of early tumors, by analyzing the electromagnetic field behavior of the implant. The tumor was modeled under CST Microwave Studio, using Wisconsin Diagnosis Breast Cancer Dataset. Features like the radius, texture, perimeter, area, and smoothness of the tumor are included along with their label data to determine whether the external shape has malignant or benign physiognomies. An explanation of the feasibility of the system deployment and technical recommendations to avoid interference is also described.