4.5 Novel Reverse Design Method of Tag Antenna Based on Image Analysis
145
The specific absorption rate (SAR) describes the absorption characteristics of the
medium to the electromagnetic field energy. SAR is defined as the rate of absorption
of electromagnetic energy by a medium in the same environment. The medium can be
regarded as a conductive medium with a dielectric constant of σ . When the effective
amplitude of the electric field strength in the medium is E, the power loss value of
the microwave in a unit volume of biological tissue can be expressed by Eq. (4.44):
P = σ
E
2
2
(4.44)
The SAR image can visually display the intensity of the radiant energy of the
antenna in a certain direction. Its intensity is related to antenna loss and gain. By
observing and processing the antenna SAR image, the antenna tag can be reverse
engineered to optimize the antenna radiation capability. By processing the SAR
images, some important parameters of the radiation performance of the RFID antenna
are obtained. These parameters can provide a basis for antenna design.
The general process of antenna SAR image processing is as follows. The SAR
images of the tag antenna in a vacuum environment are extracted and binarized.
Most of the original images are converted into binary information, thereby reducing
unnecessary information in the image. The images are averagely filtered to eliminate
image noise.
Among them, when the original images are relatively clear, the SAR images can
be directly converted into a binary image, and a sharp contour can be formed after
extracting the edge. However, some SAR images are generally not particularly clear.
They need to be converted to a grayscale image, which is then filled with grayscale
and then converted into a binary image.
Threshold segmentation is used for the preprocessed image. Determining a gray
threshold T , the divided image can be expressed by
g(x, y) =
1, f (x, y) ≥ T
0, f (x, y) < T
(4.45)
where f (x, y) is the input image and g(x, y) is the output image. If the pixel in f (x, y)
is greater than the set threshold, the region is the target image region. Otherwise, it
belongs to the background area.
After the image is edge-detected, the feature value of the image is extracted. The
eigenvalues can better express the main features and attributes of the target area. In
this paper, the area, center of gravity, perimeter, and eccentricity are selected as the
features of the image.
The image feature value is calculated as follows. Suppose the image size is M ×N ,
the coordinates of a certain point are (x, y), f (x, y) represents the gray value of the
SAR image at the coordinates, O(x, y) represents the coordinate information located
145
The specific absorption rate (SAR) describes the absorption characteristics of the
medium to the electromagnetic field energy. SAR is defined as the rate of absorption
of electromagnetic energy by a medium in the same environment. The medium can be
regarded as a conductive medium with a dielectric constant of σ . When the effective
amplitude of the electric field strength in the medium is E, the power loss value of
the microwave in a unit volume of biological tissue can be expressed by Eq. (4.44):
P = σ
E
2
2
(4.44)
The SAR image can visually display the intensity of the radiant energy of the
antenna in a certain direction. Its intensity is related to antenna loss and gain. By
observing and processing the antenna SAR image, the antenna tag can be reverse
engineered to optimize the antenna radiation capability. By processing the SAR
images, some important parameters of the radiation performance of the RFID antenna
are obtained. These parameters can provide a basis for antenna design.
The general process of antenna SAR image processing is as follows. The SAR
images of the tag antenna in a vacuum environment are extracted and binarized.
Most of the original images are converted into binary information, thereby reducing
unnecessary information in the image. The images are averagely filtered to eliminate
image noise.
Among them, when the original images are relatively clear, the SAR images can
be directly converted into a binary image, and a sharp contour can be formed after
extracting the edge. However, some SAR images are generally not particularly clear.
They need to be converted to a grayscale image, which is then filled with grayscale
and then converted into a binary image.
Threshold segmentation is used for the preprocessed image. Determining a gray
threshold T , the divided image can be expressed by
g(x, y) =
1, f (x, y) ≥ T
0, f (x, y) < T
(4.45)
where f (x, y) is the input image and g(x, y) is the output image. If the pixel in f (x, y)
is greater than the set threshold, the region is the target image region. Otherwise, it
belongs to the background area.
After the image is edge-detected, the feature value of the image is extracted. The
eigenvalues can better express the main features and attributes of the target area. In
this paper, the area, center of gravity, perimeter, and eccentricity are selected as the
features of the image.
The image feature value is calculated as follows. Suppose the image size is M ×N ,
the coordinates of a certain point are (x, y), f (x, y) represents the gray value of the
SAR image at the coordinates, O(x, y) represents the coordinate information located