Summary of Robust Localization Of Key Fob Using Channel Impulse Response Of Ultra Wide Band Sensors For Keyless Entry Systems, by Abhiram Kolli et al.
Robust Localization of Key Fob Using Channel Impulse Response of Ultra Wide Band Sensors for Keyless Entry Systems
by Abhiram Kolli, Filippo Casamassima, Horst Possegger, Horst Bischof
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 This paper explores the use of neural networks for secure keyless entry in cars, focusing on the localization of key fobs within and surrounding the vehicle. The researchers examine the performance of pre-computed features from neural networks based on ultra-wideband (UWB) localization classification as a baseline for their experiments. They also investigate the robustness of various neural networks to adversarial examples without any specific training, and propose a multi-head self-supervised neural network architecture that outperforms the baseline models. The proposed model achieves improved performance by up to 67% in certain scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special kinds of computer programs called neural networks to keep cars safe. It’s like having a super smart lock on your car door. The researchers tested different types of these neural networks to see how well they work. They found that some are better than others at recognizing when someone is trying to get into the car without permission. They also came up with a new way of building these neural networks that makes them even more secure. |
Keywords
* Artificial intelligence * Classification * Neural network * Self supervised
