High Efficiency X-Band Series-Fed Microstrip Array Antenna

Shirkolaei, Morteza Mohammadi

doi:10.2528/pierc20061003

Cited by 21 publications

(7 citation statements)

References 32 publications

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“…Also, parallel feeding increases the dimensions of the antenna structure. Additional specifications of series and parallel feedings are given in references (Mohammadi Shirkolahi, 2020a, 2020b; Mohammadi Shirkolahi, Dalili Oskoui et al., 2021).…”

Section: Fabrication and Measurementmentioning

confidence: 99%

Feature Enhancement of Three Log‐Periodic Antennas Based on Antipodal Vivaldi Patch

Eshaghpanah

Mohajeri

2022

Radio Science

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With the proliferation of communications, the need to build small, compact, multi-band, and high-gain antennas are clearly felt. Therefore, researchers make great efforts to improve the radiation characteristics and reduce the size of the antenna (Lee et al., 2016). Over the past few decades, microstrip antennas have been widely used in this field. Microstrip antennas have advantages over conventional antennas, such as their small size, which is used in wireless devices, airplanes, and satellites. Various methods reduce the dimensions and increase the microstrip antenna gain (Balanis, 2005).Common methods for designing a multi-band antenna include fractalization, the use of various slots, incomplete ground structure, and parasitic elements. Also, antenna miniaturization methods include the use of high dielectric constant materials, short-circuit microstrip antennas, shorting posts, and the use of cross-slots. The use of materials with high dielectric constant causes surface waves and reduces bandwidth and efficiency. In the shorting post method, the input impedance of the antenna power supply is sensitized to the distance between the shorting posts and the power cable. The method of cross-slots of equal length in the center of the patch is used in double-band antennas. All methods have problems such as decreasing gain and increasing the size of the antenna (

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Section: Fabrication and Measurementmentioning

confidence: 99%

Feature Enhancement of Three Log‐Periodic Antennas Based on Antipodal Vivaldi Patch

Eshaghpanah

Mohajeri

2022

Radio Science

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“…The continuous implementation of new advanced functionalities and the miniaturization of electronic devices for embedded systems has become a serious difficulty in terms of electromagnetic compatibility (EMC), especially in those than integrates communication modules [10][11][12]. This evolution usually involves a higher component integration, printed circuit board (PCB) size and thickness reduction, and the miniaturization of the device housing [13,14].…”

Section: Introductionmentioning

confidence: 99%

Design and Study of a Wide-Band Printed Circuit Board Near-Field Probe

et al. 2021

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Magnetic near-field probes (NFP) represent a suitable tool to measure the magnetic field level from a small electromagnetic interference (EMI) source. This kind of antenna is useful as a magnetic field probe for pre-compliance EMC measurements or debugging tasks since the user can scan a printed circuit board (PCB) looking for locations with strong magnetic fields. When a strong H-field point is found, the designer should check the PCB layout and components placement in that area to detect if this could result in an EMI source. This contribution focuses on analyzing the performance of an easy to build and low-cost H-field NFP designed and manufactured using a standard PCB stack-up. Thereby, the frequency range and sensitivity of the NFP-PCB are analyzed through a Finite Element Method (FEM) simulation model that makes it possible to evaluate its sensibility and effective frequency range. The numerical results obtained with the FEM models are validated against measurements to verify the design and performance of our NFP. The FEM model reproduces the experimental procedure, which is used to evaluate the performance of the NFP in terms of sensitivity by means of the simulated near-field distribution. The NFP-PCB has almost a flat response from 180 MHz to 6 GHz, with an almost perfect concordance between numerical and experimental S21 results. The numerical results show an average transmission loss of −27.9 dB by considering the flat response bandwidth, whereas the experimental one is −29.7 dB. Finally, the designed NFP is compared to two high-quality commercial probes in order to analyze its performance.

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“…Recently, several compact slotted UWB patch antennas were designed for microwave breast tumor detection 11,12 . Some novel antenna configurations were proposed to provide high efficiency and a wide operating frequency band 13–16 . In addition, metamaterial techniques have a strong impact on antenna array design 17,18 .…”

Section: Introductionmentioning

confidence: 99%

“…11,12 Some novel antenna configurations were proposed to provide high efficiency and a wide operating frequency band. [13][14][15][16] In addition, metamaterial techniques have a strong impact on antenna array design. 17,18 Alibakhshikenari et al proposed a planar antenna-array inspired by the metamaterial concept, and tumor detection effect of the proposed antenna is better than that of a standard patch antenna.…”

Section: Introductionmentioning

confidence: 99%

Microwave breast tumor localization using wavelet feature extraction and genetic algorithm‐neural network

Xiao

Liu

et al. 2021

Medical Physics

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Purpose: Ultra-Wide Band (UWB) microwave breast cancer detection is a promising new technology for routine physical examination and home monitoring. The existing microwave imaging algorithms for breast tumor detection are complex and the effect is still not ideal, due to the heterogeneity of breast tissue, skin reflection, and fibroglandular tissue reflection in backscatter signals. This study aims to develop a machine learning method to accurately locate breast tumor. Methods: A microwave-based breast tumor localization method is proposed by time-frequency feature extraction and neural network technology. First, the received microwave array signals are converted into representative and compact features by 4-level Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). Then, the Genetic Algorithm-Neural Network (GA-NN) is developed to tune hyper-parameters of the neural network adaptively. The neural network embedded in the GA-NN algorithm is a four-layer architecture and 10-fold cross-validation is performed. Through the trained neural network, the tumor localization performance is evaluated on four datasets that are created by FDTD simulation method from 2-D MRI-derived breast models with varying tissue density, shape, and size. Each dataset consists of 1000 backscatter signals with different tumor positions, in which the ratio of training set to test set is 9:1. In order to verify the generalizability and scalability of the proposed method, the tumor localization performance is also tested on a 3-D breast model. Results: For these 2-D breast models with unknown tumor locations, the evaluation results show that the proposed method has small location errors, which are 0.6076 mm, 3.0813 mm, 2.0798 mm, and 3.2988 mm, respectively, and high accuracy, which is 99%, 80%, 94%, and 85%, respectively. Furthermore, the location error and the prediction accuracy of the 3-D breast model are 3.3896 mm and 81%. Conclusions: These evaluation results demonstrate that the proposed machine learning method is effective and accurate for microwave breast tumor localization. The traditional microwave-based breast cancer detection method is to reconstruct the entire breast image to highlight the tumor. Compared with the traditional method, our proposed method can directly get the breast tumor location by applying neural network to the received microwave array signals, and circumvent any complicated image reconstruction processing.

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High Efficiency X-Band Series-Fed Microstrip Array Antenna

Cited by 21 publications

References 32 publications

Feature Enhancement of Three Log‐Periodic Antennas Based on Antipodal Vivaldi Patch

Feature Enhancement of Three Log‐Periodic Antennas Based on Antipodal Vivaldi Patch

Design and Study of a Wide-Band Printed Circuit Board Near-Field Probe

Microwave breast tumor localization using wavelet feature extraction and genetic algorithm‐neural network

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