Peel-off and Stick Antennas for Small Unmanned Aerial Vehicles

Vijayamohanan, Jayakrishnan; Ayoub, F.; Patriotis, M.; Christodoulou, C.G.; Lyke, James

doi:10.1109/apusncursinrsm.2019.8888388

Cited by 1 publication

(1 citation statement)

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“…To assess the performance of the proposed methodology, we consider the problem of designing the LPFDA antenna for specific ranges of Q-factor values. The designs that are of interest to us are based on bounds that were established, through many rounds of optimization, on certain geometric features of LPFDA that would result in desirable Q-factor values in certain UAV applications [21], [22]. Using an EM simulation software, various designs for two separate Q-factor ranges are generated and are used to train the two alternative architectures described above.…”

Section: Research Methodology a Motivationmentioning

confidence: 99%

Antenna Design Using a GAN-Based Synthetic Data Generation Approach

Noakoasteen

Vijayamohanan

Gupta

et al. 2022

IEEE Open J. Antennas Propag.

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In this paper, we propose the use of GANs as learned, data-driven knowledge database that can be queried for rapid synthesis of suitable antenna designs given a desired response. As an example, we consider the problem of designing the Log-Periodic Folded Dipole Array (LPFDA) antenna for two non-overlapping ranges of Q-factor values. By representing the antenna with the vector of its structural parameters and considering each desirable range of the Q-factor as a class, we transform our problem to that of generating new samples from a given class. We develop two alternative models, a Conditional Wasserstein GAN and a label-switched library of vanilla Wasserstein GANs and train them with a dataset of features and their associated labels (parameter vectors and Q-factor range). The main component of these models is a generator network that learns to map a normally distributed noise vector along with a binary label to the vector of parameters of candidate structures. We demonstrate that in inference mode, these models can be relied upon for fast generation of suitable designs.

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Section: Research Methodology a Motivationmentioning

confidence: 99%