Summary of Image Classifier Based Generative Method For Planar Antenna Design, by Yang Zhong et al.
Image Classifier Based Generative Method for Planar Antenna Design
by Yang Zhong, Weiping Dou, Andrew Cohen, Dia’a Bisharat, Yuandong Tian, Jiang Zhu, Qing Huo Liu
First submitted to arxiv on: 16 Dec 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method simplifies antenna design on printed circuit boards (PCBs) by modeling PCB antennas using basic components. The approach involves two steps: determining geometric dimensions and positions of the components without requiring expertise. Random sampling statistics guide dimension selection, while a convolutional neural network (CNN)-based image classifier determines component positions. The workflow is demonstrated through wearable product examples, showcasing realistic designs with performance metrics comparable to those designed by experienced engineers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps make antenna design on printed circuit boards easier for many people. It uses simple components and two steps to create prototypes without needing special knowledge. The method picks the right dimensions using statistics and chooses where the components go using a special kind of AI called a convolutional neural network (CNN). This approach is shown to work well with real designs from wearable products, which are as good as those made by experts. |
Keywords
* Artificial intelligence * Cnn * Neural network
