The RFI Fast Mitigation Algorithm Based on Block LMS Filter

Abstract

The radio telescope possesses high sensitivity and strong signal collection capabilities. While receiving celestial radiation signals, it also captures Radio Frequency Interferences (RFI) introduced by human activities. RFI, as signals originating from sources other than the astronomical targets, significantly impacts the quality of astronomical data. This paper presents an RFI fast mitigation algorithm based on block Least Mean Square 
(LMS) algorithm. It enhances the traditional adaptive LMS filter by grouping L adjacent time sampled points into one block and applying the same filter coefficients for filtering within each block. This transformation reduces multiplication calculations and enhances algorithm efficiency by leveraging the time-domain convolution theorem. The algorithm is tested using baseband data from the Parkes 64-meter radio telescope’s pulsar observations and simulated data. The results confirm the algorithm’s effectiveness, as the pulsar profile after RFI mitigation closely matches the original pulsar profile.

FetchEEG: a hybrid approach combining feature extraction and temporal-channel joint attention for EEG-based emotion classification

“…Numerous studies have focused on exploring effective EEG features for classification, and the recent advancement of machine learning methods and technologies has significantly contributed to the further development of traditional methods. There have been certain attempts at the application of common spatial pattern (CSP) algorithms [22], such as the FBCSP algorithm [23][24][25], which filter signals using filter banks, compute the CSP energy features for each signal output using the time filters, and, finally, select and classify the obtained features. Despite the improvements over the original CSP method, these algorithms still suffer from a lack of consideration for temporal dynamics.…”