Machine Learning based Low-Scale Dipole Antenna Optimization using Bootstrap Aggregation

Neelamraju, Pavan Mohan; Pothapragada, Pranav; Rana, Goutam; Chaturvedi, Divya; Kumar, Rupesh

doi:10.1109/pcems58491.2023.10136108

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…The method used in this research is Bootstrap [17][18][19][20][21][22][23]. The bootstrap method is a resampling technique used to estimate statistics on a population by sampling the dataset with replacement.…”

Section: Methodsmentioning

confidence: 99%

“…It can be used to estimate summary statistics such as the mean or standard deviation, and It is used in Machine Learning to estimate the skill of machine learning models when making predictions on data not included in the training data. One of the methods used is Bootstrap [15][16][17][18], as shown in Figure 1. Bootstrap is a widely used statistical tool that is very powerful in quantifying the uncertainty associated with a given estimator or statistical learning method.…”

Section: Methodsmentioning

confidence: 99%

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The Relationship of Teacher Activity in the Teaching and Learning Process to Elementary Student Learning Outcomes Using Bootstrap Machine Learning

Hia,

Sihombing,

Juledi

2023

IOTA

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Often, after the learning and teaching process is over, students will be tested with quizzes, midterm exams, and even end-of-semester exams, but these exams still take time after the teacher has taught several weeks or months that have passed; what if after teaching, for example, a math lesson, and students immediately understand or do not understand at all, and this can be detected using Machine Learning. The variable that can be raised is the value or quiz grade of a particular subject; for example, mathematics is one of the disliked subjects for most elementary school students, but how to find out that the student is able or unable to solve math problems and predict the end of semester grades for mathematics, this can be determined using Machine Learning, using the KNN Algorithm or K-MEANS method, or other methods that are deemed appropriate to the existing case study. In this case study, it is predicted whether a variable affects each other or affects other variables; this is done by doing or drawing relationships between variables. This research successfully concluded from the performance of machine learning in predicting students' understanding of math lessons after teaching and learning activities ended. The parameters that will be used for testing are population and sampling, and then data analysis, validity, and reliability tests are carried out.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The Relationship of Teacher Activity in the Teaching and Learning Process to Elementary Student Learning Outcomes Using Bootstrap Machine Learning

Hia,

Sihombing,

Juledi

2023

IOTA

View full text Add to dashboard Cite

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“…A range of methods for impedance tuning and bandwidth enhancement have been proposed, including the use of wideband impedance matching balun for balanced two-arm antennas [13], and the design of an optimal matching stack that provides flat broadband transmission [14] Finally, a range of optimization techniques have been proposed to improve the performance of dipole antennas. These include machine learning-based approaches [15], computationally-efficient design optimization methods using accelerated gradient search algorithms [16], and electromagnetic bottom-up optimization strategies for automated antenna designs [17]. These collectively demonstrate the potential of various numerical simulation, electromagnetic modeling, and optimization algorithms in enhancing the performance of dipole antennas.…”

Section: Introductionmentioning

confidence: 95%

Performance Analysis of Dipole Antenna Applications: A Literature Review

Sosa,

Arboleda

2024

Preprint

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Dipole antennas, fundamental components in wireless communication systems, have garnered significant attention due to their versatility and efficiency across various applications. This literature review explores the performance analysis of dipole antennas, focusing on recent advancements and methodologies from 2019 to 2024. Employing a systematic approach, the review synthesizes findings from multiple databases, emphasizing peer-reviewed studies that delve into dipole antenna designs and applications. Through meticulous data extraction, key parameters influencing dipole antenna performance, such as material properties and operating frequency, are identified and analyzed. Results highlight the widespread utilization of dipole antennas in wireless communication systems, including indoor positioning, mobile communication, and antenna calibration, among others. Discussions underscore the evolution of dipole antennas in base station environments and their role in filtering applications. The review concludes by emphasizing the continued relevance and evolution of dipole antennas, showcasing their adaptability and potential across diverse wireless communication contexts.

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