Multiobjective genetic algorithm approach for a dual-feed circular polarised patch antenna design

2000 ◽  
Vol 36 (12) ◽  
pp. 1005 ◽  
Author(s):  
B. Aljibouri ◽  
E.G. Lim ◽  
H. Evans ◽  
A. Sambell
Keyword(s):  
Genetic Algorithm ◽  
Patch Antenna ◽  
Antenna Design ◽  
Multiobjective Genetic Algorithm ◽  
Genetic Algorithm Approach

Multi-frequency patch antenna design via the method of moment and genetic algorithm

2002 ◽  
Vol 35 (3) ◽  
pp. 184-186 ◽  
Author(s):  
Filiberto Bilotti ◽  
Francesco Castellana ◽  
Lucio Vegni
Keyword(s):  
Genetic Algorithm ◽  
Patch Antenna ◽  
Antenna Design ◽  
Method Of Moment

Multi-factor product authentication using integrated Quick Response (QR) code pixelated antenna

2017 ◽  
Vol 2017 (1) ◽  
pp. 000608-000612
Author(s):  
John Doroshewitz ◽  
Amanpreet Kaur ◽  
Jeffrey Nanzer ◽  
Premjeet Chahal
Keyword(s):  
Genetic Algorithm ◽  
Radio Frequency ◽  
Design Process ◽  
Patch Antenna ◽  
Microstrip Patch Antenna ◽  
Antenna Design ◽  
Qr Code ◽  
Quick Response ◽  
Microstrip Patch

Abstract A Quick Response (QR) Code style antenna is presented. Such an antenna can provide two-level authentication, both optically through the current QR technology, plus a radio frequency (RF) signature from microstrip patch antenna designed from the QR signature using copper patterning. The antenna is designed through the pixilation of a patch antenna where conductor is present in the dark regions of the optical QR code. The QR based antenna design has a unique frequency and radiation signature and can be used for RF authentication of products. The design process for the pixilation is presented as well as fabrication and measured results of a QR code antenna design. The possibility of using a Genetic Algorithm to create a “library” of acceptable antenna results in accordance with the QR data it represents is also discussed.

scholarly journals Optimization and analysis of high gain wideband microstrip patch antenna using genetic algorithm

2017 ◽  
Vol 7 (1.5) ◽  
pp. 176 ◽  
Author(s):  
Raj Gaurav Mishra ◽  
Ranjan Mishra ◽  
Piyush Kuchhal ◽  
N. Prasanthi Kumari
Keyword(s):  
Genetic Algorithm ◽  
Patch Antenna ◽  
High Gain ◽  
Microstrip Antennas ◽  
Microstrip Patch Antenna ◽  
Antenna Design ◽  
Microstrip Patch ◽  
X Band ◽  
Communication Devices ◽  
Voice Data

Microstrip antennas that can operate in single and multiple frequency bands are required in various wireless communication devices. A single patch, square shaped microstrip patch antenna having high directivity and gain is proposed in this paper. The geometry of proposed antenna is optimized using Genetic Algorithm (GA) to operate in X-Band for wideband applications. The proposed antenna design exhibits a wide operating bandwidth 550 MHz (simulated) and 450 MHz (measured), high gain and directivity of about 8.35 dB (simulated) making it suitable for wideband applications. The proposed antenna design works in X-band which has weatherproof characteristics and supports easy communication of voice, data, images and HD videos. The attractiveness of the GA design over the traditional design methods is its ability to achieve the desired performance by using a simple design of single patch antenna.

scholarly journals Evolutionary Computing based Neuron-Computational Model for Microstrip Patch Antenna Design Optimization

2021 ◽  
Vol 13 (03) ◽  
pp. 15-40
Author(s):  
Rohini Saxena ◽  
Mukesh Kumar ◽  
Shadman Aslam
Keyword(s):  
Genetic Algorithm ◽  
Patch Antenna ◽  
Evolutionary Computing ◽  
Microstrip Patch Antenna ◽  
Ultra Wideband ◽  
Antenna Design ◽  
Design Parameters ◽  
Adaptive Genetic Algorithm ◽  
Microstrip Patch ◽  
Depth Analysis

In this paper, a novel Evolutionary Computing named Adaptive Genetic Algorithm (AGA) based ANN model is developed for rectangular MPA (Microstrip patch antenna). Considering at-hand and Nextgeneration Ultra wideband application demands, the emphasis has been made on retaining optimal lowcost design with desired cut-off frequency. The proposed method employs multiple sets of theoreticallydriven training instances or patch antenna design parameters which have been processed for normalization and sub-sampling to achieve a justifiable and reliable sample size for further design parameter prediction. Procedurally, the input design parameters were processed for normalization followed by sub-sampling to give rise to a sufficient set of inputs to perform knowledge-driven (designparameter) prediction. Considering limitations of the major at-hand machine learning methods which often undergo local minima and convergence while training, we designed a state-of-art new Adaptive Genetic Algorithm based neuro-computing model (AGA-ANN), which helped to predict the set of optimal design parameters for rectangular microstrip patch antenna. The predicted patch antenna length and width values were later used for verification which achieved the expected frequency. The depth analysis revealed that a rectangular patch antenna with width 14.78 mm, length 11.08mm, feed-line 50 Ω can achieve the cut-off frequency of 8.273 GHz, which can be of great significance for numerous UWB applications.

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