Investigation of New Algorithms for Estimation of Losses in Microwave Devices Based on a Waveguide or a Meander Line

Models of open cylindrical multilayer gyroelectric-anisotropic-gyroelectric waveguides are presented in this paper. The influence of density of free carriers, temperature and the presence of the external dielectric layer on the wave phase characteristics of the models of n-GaAs waveguides has been evaluated. Differential Maxwell’s equations, coupled mode and partial area methods have been used to obtain complex dispersion equation of the models of gyroelectric-anisotropic-gyroelectric waveguides with or without the temperature sensitive external anisotropic dielectric layer. The analysis has shown that the phase characteristics are practically unchanged when the density of electrons is equal to N = (1017–5·1018) m−3, d/rs = 0, the changes of wave phase coefficients are obtained in the models of waveguides with the external anisotropic dielectric layer. The largest differences of wave phase coefficient are obtained when the density of electrons is N = 1021 m−3. The external dielectric layer improves the control of gyroelectric n-GaAs waveguides with temperature.

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“…The analysis of the presented model shows that the developed model is more universal than presented in other works [17], [18].…”

Section: A Methodsmentioning

confidence: 80%

Phase Characteristics of Models of GaAs Gyroelectric Waveguides with Temperature Sensitive Anisotropic Dielectric Layers in Case of One Layer

Plonis

Katkevičius

Belova-Plonienė

2018

Electrical, Control and Communication Engineering

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“…1, and its detailed description can be found in [24]. It was shown in [25,26] that duration of calculations during analysis of the MDSs increases considerably when it is necessary to solve complex dispersion equation. Number of dispersion equations depends on the model of the meander system.…”

Section: Methodsmentioning

confidence: 99%

Delay Systems Synthesis using Multi-Layer Perceptron Network

et al. 2018

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The aim of this paper is to accelerate development and investigation of the delay systems. The computational time for investigation of particular design of delay system may take from several minutes up to several days. To achieve the required constructional parameters of the system, the iterative calculations usually should be repeated many times. In this paper, an artificial neural network is proposed to be used as the universal approximator for solving mathematical problems of delay system investigation instead of usual analytical and numerical techniques. The application of a multi-layer perceptron is proposed for approximation of solution space with discrete estimates, which were initially received by application of numerical techniques. Different structures of the multi-layer perceptron were tested for approximation. The difference between delay systems synthesis, which was estimated using numerical techniques and trained multi-layer perceptron did not exceed 5% for any of the six design parameter values. The execution time for estimating single delay system was reduced from 240 s to 20 ms. Such fast estimation of design parameters enables performing delay system analysis and design in real time, preserving time for structure visualization in 3D or virtual reality environment.

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“…In the domain of computer vision, the ability to detect and analyze specific objects within images is central to a multitude of applications, from augmented reality systems to quality control in manufacturing processes [1][2][3][4][5][6][7][8][9]. One of the seemingly simple but critical objects that demand accurate detection is the measurement ruler.…”

Section: Introductionmentioning

confidence: 99%

Rulers2023: An Annotated Dataset of Synthetic and Real Images for Ruler Detection Using Deep Learning

Matuzevičius

2023

Electronics

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This research investigates the usefulness and efficacy of synthetic ruler images for the development of a deep learning-based ruler detection algorithm. Synthetic images offer a compelling alternative to real-world images as data sources in the development and advancement of computer vision systems. This research aims to answer whether using a synthetic dataset of ruler images is sufficient for training an effective ruler detector and to what extent such a detector could benefit from including synthetic images as a data source. The article presents the procedural method for generating synthetic ruler images, describes the methodology for evaluating the synthetic dataset using trained convolutional neural network (CNN)-based ruler detectors, and shares the compiled synthetic and real ruler image datasets. It was found that the synthetic dataset yielded superior results in training the ruler detectors compared with the real image dataset. The results support the utility of synthetic datasets as a viable and advantageous approach to training deep learning models, especially when real-world data collection presents significant logistical challenges. The evidence presented here strongly supports the idea that when carefully generated and used, synthetic data can effectively replace real images in the development of CNN-based detection systems.

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Investigation of New Algorithms for Estimation of Losses in Microwave Devices Based on a Waveguide or a Meander Line

Cited by 4 publications

References 31 publications

Phase Characteristics of Models of GaAs Gyroelectric Waveguides with Temperature Sensitive Anisotropic Dielectric Layers in Case of One Layer

Phase Characteristics of Models of GaAs Gyroelectric Waveguides with Temperature Sensitive Anisotropic Dielectric Layers in Case of One Layer

Delay Systems Synthesis using Multi-Layer Perceptron Network

Rulers2023: An Annotated Dataset of Synthetic and Real Images for Ruler Detection Using Deep Learning

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