Measurement of Complex Permittivity using Artificial Neural Networks

2011 ◽  
Vol 53 (1) ◽  
pp. 200-203 ◽  
Author(s):  
Azhar Hasan ◽  
Andrew F. Peterson
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Complex Permittivity ◽  
Artificial Neural

Detecting heavy metal contamination in soil using complex permittivity and artificial neural networks

2004 ◽  
Vol 41 (6) ◽  
pp. 1054-1067 ◽  
Author(s):  
J Q Shang ◽  
W Ding ◽  
R K Rowe ◽  
L Josic
Keyword(s):  
Heavy Metals ◽  
Neural Networks ◽  
Heavy Metal ◽  
Artificial Neural Networks ◽  
Complex Permittivity ◽  
Heavy Metal Contamination ◽  
Zinc Salt ◽  
Ann Model ◽  
Wide Range ◽  
Artificial Neural

The use of the complex permittivity, an intrinsic electrical property of materials, to detect the presence and type of heavy metals in soil is investigated. The soil specimens are prepared by mixing the soil with distilled and deionized water, NaCl solutions, and copper and zinc salt solutions and compacting at known water contents. The complex permittivities of the soil specimens are measured in the laboratory using a custom-developed apparatus. A database, which includes both contaminated and uncontaminated soil specimens, is developed, with the soil water content, density, and pore-fluid salinity varying over a relatively wide range. Two artificial neural network (ANN) models are developed to (i) identify whether the heavy metals are present in the soil; and, if so, (ii) distinguish the metal type, based on the complex permittivities measured on the soil specimens. The first ANN model (identification) can correctly identify the presence of heavy metals in 90% of cases. The second ANN model (classification) can correctly classify the type of the heavy metal in 95% of cases. Better performance can be achieved if more complex permittivity data are available for the training of the networks.Key words: heavy metals, soil contamination, contamination detection, complex permittivity, artificial neural networks.

scholarly journals Application of Artificial Neural Networks for Accurate Determination of the Complex Permittivity of Biological Tissue

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4640
Author(s):  
Julian Bonello ◽  
Andrea Demarco ◽  
Iman Farhat ◽  
Lourdes Farrugia ◽  
Charles V. Sammut
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Complex Permittivity ◽  
Accurate Determination ◽  
Coaxial Line ◽  
Biological Tissues ◽  
Measurement Procedure ◽  
Tissue Samples ◽  
Therapeutic Modalities ◽  
Artificial Neural

Medical devices making use of radio frequency (RF) and microwave (MW) fields have been studied as alternatives to existing diagnostic and therapeutic modalities since they offer several advantages. However, the lack of accurate knowledge of the complex permittivity of different biological tissues continues to hinder progress in of these technologies. The most convenient and popular measurement method used to determine the complex permittivity of biological tissues is the open-ended coaxial line, in combination with a vector network analyser (VNA) to measure the reflection coefficient (S11) which is then converted to the corresponding tissue permittivity using either full-wave analysis or through the use of equivalent circuit models. This paper proposes an innovative method of using artificial neural networks (ANN) to convert measured S11 to tissue permittivity, circumventing the requirement of extending the VNA measurement plane to the coaxial line open end. The conventional three-step calibration technique used with coaxial open-ended probes lacks repeatability, unless applied with extreme care by experienced persons, and is not adaptable to alternative sensor antenna configurations necessitated by many potential diagnostic and monitoring applications. The method being proposed does not require calibration at the tip of the probe, thus simplifying the measurement procedure while allowing arbitrary sensor design, and was experimentally validated using S11 measurements and the corresponding complex permittivity of 60 standard liquid and 42 porcine tissue samples. Following ANN training, validation and testing, we obtained a prediction accuracy of 5% for the complex permittivity.

Using complex permittivity and artificial neural networks to identify and classify copper, zinc, and lead contamination in soil

2006 ◽  
Vol 43 (1) ◽  
pp. 100-109 ◽  
Author(s):  
F Amegashie ◽  
J Q Shang ◽  
E K Yanful ◽  
W Ding ◽  
S Al-Martini
Keyword(s):  
Heavy Metals ◽  
Neural Networks ◽  
Heavy Metal ◽  
Artificial Neural Networks ◽  
Complex Permittivity ◽  
Soil Samples ◽  
Lead Contamination ◽  
Subsurface Contamination ◽  
Ann Models ◽  
Artificial Neural

Complex permittivity measurements combined with artificial neural networks (ANNs) are investigated as a method for assessing and identifying heavy metal contamination in soil. The measurements are carried out with a custom-built device on 164 compacted samples of a natural clayey soil, artificially contaminated with various simple salts including heavy metals (Cu, Zn, and Pb). The soil samples are prepared by mixing solutions of the various salts with the soil at various concentrations and water contents. A database has been set up consisting of complex per mittivity measurements made between the frequencies of 200 and 500 MHz and measured physical and chemical properties of the soil samples. Using this database as input, two ANN models are designed, the first to detect the presence or absence of heavy metals in the soil samples and the second to determine whether the heavy metal, if present in a given sample, is Cu, Zn, or Pb. Both ANN models perform reasonably well. Overall, the first model is able to detect the presence of heavy metals in 92.7% of cases, and the second is successful in distinguishing the particular type of heavy metal in 76.4% of all the samples containing heavy metals. These encouraging results underscore the potential of complex permittivity and ANNs as promising tools for nondestructive subsurface contamination assessment.Key words: heavy metals, subsurface contamination, complex permittivity, artificial neural networks, contaminant detection.

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