Supervised Radio Frequency Interference Detection with SNNs
- URL: http://arxiv.org/abs/2406.06075v1
- Date: Mon, 10 Jun 2024 07:49:51 GMT
- Title: Supervised Radio Frequency Interference Detection with SNNs
- Authors: Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun, Richard Dodson,
- Abstract summary: Radio Frequency Interference (RFI) poses a significant challenge in radio astronomy, arising from terrestrial and celestial sources, disrupting observations conducted by radio telescopes.
Given the dynamic and temporal nature of radio astronomy observations, Spiking Neural Networks (SNNs) emerge as a promising approach.
We study the encoding of radio astronomy visibility data for SNN inference, considering six encoding schemes: rate, latency, delta-modulation, and three variations of the step-forward algorithm.
- Score: 25.08630315149258
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Radio Frequency Interference (RFI) poses a significant challenge in radio astronomy, arising from terrestrial and celestial sources, disrupting observations conducted by radio telescopes. Addressing RFI involves intricate heuristic algorithms, manual examination, and, increasingly, machine learning methods. Given the dynamic and temporal nature of radio astronomy observations, Spiking Neural Networks (SNNs) emerge as a promising approach. In this study, we cast RFI detection as a supervised multi-variate time-series segmentation problem. Notably, our investigation explores the encoding of radio astronomy visibility data for SNN inference, considering six encoding schemes: rate, latency, delta-modulation, and three variations of the step-forward algorithm. We train a small two-layer fully connected SNN on simulated data derived from the Hydrogen Epoch of Reionization Array (HERA) telescope and perform extensive hyper-parameter optimization. Results reveal that latency encoding exhibits superior performance, achieving a per-pixel accuracy of 98.8% and an f1-score of 0.761. Remarkably, these metrics approach those of contemporary RFI detection algorithms, notwithstanding the simplicity and compactness of our proposed network architecture. This study underscores the potential of RFI detection as a benchmark problem for SNN researchers, emphasizing the efficacy of SNNs in addressing complex time-series segmentation tasks in radio astronomy.
Related papers
- SFANet: Spatial-Frequency Attention Network for Weather Forecasting [54.470205739015434]
Weather forecasting plays a critical role in various sectors, driving decision-making and risk management.
Traditional methods often struggle to capture the complex dynamics of meteorological systems.
We propose a novel framework designed to address these challenges and enhance the accuracy of weather prediction.
arXiv Detail & Related papers (2024-05-29T08:00:15Z) - RFI-DRUnet: Restoring dynamic spectra corrupted by radio frequency
interference -- Application to pulsar observations [9.304820505959519]
Radio frequency interference (RFI) have been an enduring concern in radio astronomy.
This work proposes to tackle RFI mitigation as a joint detection and restoration.
arXiv Detail & Related papers (2024-02-21T15:19:09Z) - RFI Detection with Spiking Neural Networks [25.08630315149258]
This study introduces first exploratory application of Spiking Neural Networks (SNNs) to an astronomical dataprocessing task, specifically RFI detection.
We adapt the nearest-latentneighbours algorithm and auto-encoder architecture proposed by previous authors to SNN execution by direct ANN2SNN conversion.
Our approach remains competitive with existing methods in AUROC, AUPRC and F1 scores for the HERA dataset but exhibits difficulty in the LOFAR and Tabascal datasets.
arXiv Detail & Related papers (2023-11-24T06:27:08Z) - Deep learning-based deconvolution for interferometric radio transient
reconstruction [0.39259415717754914]
Radio astronomy facilities like LOFAR, MeerKAT/SKA, ASKAP/SKA, and the future SKA-LOW bring tremendous sensitivity in time and frequency.
These facilities enable advanced studies of radio transients, volatile by nature, that can be detected or missed in the data.
These transients are markers of high-energy accelerations of electrons and manifest in a wide range of temporal scales.
arXiv Detail & Related papers (2023-06-24T08:58:52Z) - NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction [79.13750275141139]
This paper proposes a novel and fast self-supervised solution for sparse-view CBCT reconstruction.
The desired attenuation coefficients are represented as a continuous function of 3D spatial coordinates, parameterized by a fully-connected deep neural network.
A learning-based encoder entailing hash coding is adopted to help the network capture high-frequency details.
arXiv Detail & Related papers (2022-09-29T04:06:00Z) - Three-Way Deep Neural Network for Radio Frequency Map Generation and
Source Localization [67.93423427193055]
Monitoring wireless spectrum over spatial, temporal, and frequency domains will become a critical feature in beyond-5G and 6G communication technologies.
In this paper, we present a Generative Adversarial Network (GAN) machine learning model to interpolate irregularly distributed measurements across the spatial domain.
arXiv Detail & Related papers (2021-11-23T22:25:10Z) - Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned
Neural Models [44.609368050610044]
Deep neural networks (DNNs) are able to "learn" intricate underlying structures from data.
In this work, an emitter radio map disaggregation-based approach is proposed.
arXiv Detail & Related papers (2021-05-01T07:04:09Z) - Learning to Beamform in Heterogeneous Massive MIMO Networks [48.62625893368218]
It is well-known problem of finding the optimal beamformers in massive multiple-input multiple-output (MIMO) networks.
We propose a novel deep learning based paper algorithm to address this problem.
arXiv Detail & Related papers (2020-11-08T12:48:06Z) - Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by
Spiking Neural Network [68.43026108936029]
We propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment.
We implement this algorithm in a real-time robotic system with a microphone array.
The experiment results show a mean error azimuth of 13 degrees, which surpasses the accuracy of the other biologically plausible neuromorphic approach for sound source localization.
arXiv Detail & Related papers (2020-07-07T08:22:56Z) - Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem.
We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection.
We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
arXiv Detail & Related papers (2020-01-10T05:29:17Z)
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.