Classification of Microwave Planar Filters by Deep Learning

Veselý, Jiří; Olivová, Jana; Gotthans, J.; Gotthans, T.; Raida, Zbyněk

doi:10.13164/re.2022.0069

Cited by 2 publications

(2 citation statements)

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“…In the last decade, thanks to the increase in computational power, and the big variety of training data, convolutional neural networks achieve very high performance on some complex visual tasks. They are used in image search services [10], [11], self-driving cars [12], [13], automatic video classifi-cation systems, medical applications [14], [15], malware classification [16], filters as bitmap objects classification [17], and others. Convolutional neural networks are not restricted to the perception of visual information, and they are strong at other tasks.…”

Section: Introductionmentioning

confidence: 99%

SAMPLE Dataset Objects Classification Using Deep Learning Algorithms

Turcanik¹,

Perdoch²

2023

RADIOENGINEERING

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The main topic of the article is automatic target classification of the synthetic aperture radar images based on the dataset composed of measured and synthetic data. The original contribution of the authors is their own topology of the convolutional neural network (CNN) with 1, 2, 3, and 4 tiers. The original convolutional neural network is used to classify radar images from the Synthetic And Measured Paired and Labeled Experiment (SAMPLE) dataset which consists of SAR imagery from publicly available datasets and well-matched synthetic data. The presented topologies of the CNN with 1, 2, 3, and 4 tiers were analyzed in 3 different scenarios: trained on the basis of real measured data and tested by synthetic data, trained on the basis of synthetic data, and tested by real measured data, and in the last case training and testing sets were formed by combining real measured and synthetic data. Based on the results of testing we could not use the proposed convolutional neural network trained with real measured data to classify synthetic radar images and vice versa (the 1 st and the 2 nd scenarios). The only last scenario with a combination of real measured and synthetic data in the training, validation, and testing data sets generates excellent results. The authors also present some confusion matrixes, which can explain the reasons for the misclassification of radar images of military equipment. Comparing achieved results with another SAMPLE dataset classification results we can prove the usability of proposed and tested CNN structures for automatic target classification of the synthetic aperture radar images. The classification accuracy of the original convolutional network is 96.1%, which is better than the results of the other research so far.

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Section: Introductionmentioning

confidence: 99%

SAMPLE Dataset Objects Classification Using Deep Learning Algorithms

Turcanik¹,

Perdoch²

2023

RADIOENGINEERING

View full text Add to dashboard Cite

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“…In [20], the classifier considered the requested gain, impedance bandwidth, and operation frequency and selected among a patch antenna, a spiral antenna, or a horn antenna. Then, the selected antenna was designed by an inverse neural model that mapped requested antenna parameters to antenna dimensions.…”

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confidence: 99%

Pre-Design of Multi-Band Planar Antennas by Artificial Neural Networks

et al. 2023

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In this communication, artificial neural networks are used to estimate the initial structure of a multiband planar antenna. The neural networks are trained on a set of selected normalized multiband antennas characterized by time-efficient modal analysis with limited accuracy. Using the Deep Learning Toolbox in Matlab, several types of neural networks have been created and trained on the sample planar multiband antennas. In the neural network learning process, suitable network types were selected for the design of these antennas. The trained networks, depending on the desired operating bands, will select the appropriate antenna geometry. This is further optimized using Newton’s method in HFSS. The use of the neural pre-design concept speeds up and simplifies the design of multiband planar antennas. The findings presented in this paper will be used to refine and accelerate the design of planar multiband antennas.

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Classification of Microwave Planar Filters by Deep Learning

Cited by 2 publications

References 0 publications

SAMPLE Dataset Objects Classification Using Deep Learning Algorithms

SAMPLE Dataset Objects Classification Using Deep Learning Algorithms

Pre-Design of Multi-Band Planar Antennas by Artificial Neural Networks

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