Robust Localization of Key Fob Using Channel Impulse Response of Ultra Wide Band Sensors for Keyless Entry Systems

• URL: http://arxiv.org/abs/2401.08863v1
• Date: Tue, 16 Jan 2024 22:35:14 GMT
• Title: Robust Localization of Key Fob Using Channel Impulse Response of Ultra Wide Band Sensors for Keyless Entry Systems
• Authors: Abhiram Kolli, Filippo Casamassima, Horst Possegger, Horst Bischof
• Abstract summary: Using neural networks for localization of key fob within and surrounding a car as a security feature for keyless entry is fast emerging. The model's performance improved by 67% at certain ranges of adversarial magnitude for fast gradient sign method and 37% each for basic iterative method and projected gradient descent method.
• Score: 12.313730356985019
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Using neural networks for localization of key fob within and surrounding a car as a security feature for keyless entry is fast emerging. In this paper we study: 1) the performance of pre-computed features of neural networks based UWB (ultra wide band) localization classification forming the baseline of our experiments. 2) Investigate the inherent robustness of various neural networks; therefore, we include the study of robustness of the adversarial examples without any adversarial training in this work. 3) Propose a multi-head self-supervised neural network architecture which outperforms the baseline neural networks without any adversarial training. The model's performance improved by 67% at certain ranges of adversarial magnitude for fast gradient sign method and 37% each for basic iterative method and projected gradient descent method.

Related papers

• Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks [49.808194368781095]
  We show that three-layer neural networks have provably richer feature learning capabilities than two-layer networks. This work makes progress towards understanding the provable benefit of three-layer neural networks over two-layer networks in the feature learning regime.
  arXiv  Detail & Related papers  (2023-05-11T17:19:30Z)
• Properties and Potential Applications of Random Functional-Linked Types of Neural Networks [81.56822938033119]
  Random functional-linked neural networks (RFLNNs) offer an alternative way of learning in deep structure. This paper gives some insights into the properties of RFLNNs from the viewpoints of frequency domain. We propose a method to generate a BLS network with better performance, and design an efficient algorithm for solving Poison's equation.
  arXiv  Detail & Related papers  (2023-04-03T13:25:22Z)
• Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth [92.25666446274188]
  sinusoidal neural networks with activations have been proposed as an alternative to networks with traditional activation functions. We first propose a simplified version of such sinusoidal neural networks, which allows both for easier practical implementation and simpler theoretical analysis. We then analyze the behavior of these networks from the neural tangent kernel perspective and demonstrate that their kernel approximates a low-pass filter with an adjustable bandwidth.
  arXiv  Detail & Related papers  (2022-11-26T07:41:48Z)
• Test-time adversarial detection and robustness for localizing humans using ultra wide band channel impulse responses [5.96002531660335]
  We propose a test-time adversarial example detector which detects the input adversarial example through quantifying the localized intermediate responses of a pre-trained neural network. In order to make the network robust, we extenuate the non-relevant features by non-iterative input sample clipping.
  arXiv  Detail & Related papers  (2022-11-10T20:21:43Z)
• Local learning through propagation delays in spiking neural networks [0.0]
  We propose a novel local learning rule for spiking neural networks in which spike propagation times undergo activity-dependent plasticity. We demonstrate the use of this method in a three-layer feedfoward network with inputs from a database of handwritten digits.
  arXiv  Detail & Related papers  (2022-10-27T13:48:40Z)
• Increasing the Accuracy of a Neural Network Using Frequency Selective Mesh-to-Grid Resampling [4.211128681972148]
  We propose the use of keypoint frequency selective mesh-to-grid resampling (FSMR) for the processing of input data for neural networks. We show that depending on the network architecture and classification task the application of FSMR during training aids learning process. The classification accuracy can be increased by up to 4.31 percentage points for ResNet50 and the Oxflower17 dataset.
  arXiv  Detail & Related papers  (2022-09-28T21:34:47Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Robustness against Adversarial Attacks in Neural Networks using Incremental Dissipativity [3.8673567847548114]
  Adversarial examples can easily degrade the classification performance in neural networks. This work proposes an incremental dissipativity-based robustness certificate for neural networks.
  arXiv  Detail & Related papers  (2021-11-25T04:42:57Z)
• Learning Neural Network Subspaces [74.44457651546728]
  Recent observations have advanced our understanding of the neural network optimization landscape. With a similar computational cost as training one model, we learn lines, curves, and simplexes of high-accuracy neural networks. With a similar computational cost as training one model, we learn lines, curves, and simplexes of high-accuracy neural networks.
  arXiv  Detail & Related papers  (2021-02-20T23:26:58Z)
• Increasing the Confidence of Deep Neural Networks by Coverage Analysis [71.57324258813674]
  This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model against different unsafe inputs. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs.
  arXiv  Detail & Related papers  (2021-01-28T16:38:26Z)
• Supervised Learning with First-to-Spike Decoding in Multilayer Spiking Neural Networks [0.0]
  We propose a new supervised learning method that can train multilayer spiking neural networks to solve classification problems. The proposed learning rule supports multiple spikes fired by hidden neurons, and yet is stable by relying on firstspike responses generated by a deterministic output layer. We also explore several distinct spike-based encoding strategies in order to form compact representations of input data.
  arXiv  Detail & Related papers  (2020-08-16T15:34:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.