An Interactive Guide to the Fourier Transform
https://betterexplained.com/articles/an-interactive-guide-to-the-fourier-transform/
#HackerNews #FourierTransform #InteractiveGuide #MathEducation #SignalProcessing
An Interactive Guide to the Fourier Transform
https://betterexplained.com/articles/an-interactive-guide-to-the-fourier-transform/
#HackerNews #FourierTransform #InteractiveGuide #MathEducation #SignalProcessing
NEW at #PIMRC2025!
๐ก We're mastering Near-Field complexity in ELAA systems.
Introducing VR-HMM-P-SOMP: a novel algorithm using HMMs to tackle spatial non-stationarity (SnS). The result? Significantly improved estimation accuracy (NMSE) in low-SNR scenarios for future #6G networks! ๐
๐ https://zenodo.org/records/17631563
#MultiX #ChannelEstimation #WirelessNetworks #SignalProcessing
this is an excellent master's thesis on transfer function estimation from noisy data
https://dspace.mit.edu/bitstream/handle/1721.1/50472/42218062-MIT.pdf;sequence=2
i love these classic signal processing papers!
`A stand-alone noise suppression algorithm is presented for reducing the spectral effects of acoustically added noise in speech. Effective performance of digital speech processors operating in practical environments may require suppression of noise from the digital wave-form. Spectral subtraction offers a computationally efficient, processor-independent approach to effective digital speech analysis. `

Audry et al., (2025). Plaquette: An Object-Oriented Framework for Embedded Signal Processing in Interactive Media. Journal of Open Source Software, 10(115), 9144, https://doi.org/10.21105/joss.09144
๐ซ Fast frequency reconstruction using Deep Learning for event recognition in ring laser data
https://arxiv.org/abs/2510.03325
#laser #optics #navigation #gyroscope #signals #signalprocessing #ml #deeplearning

The reconstruction of a frequency with minimal delay from a sinusoidal signal is a common task in several fields; for example Ring Laser Gyroscopes, since their output signal is a beat frequency. While conventional methods require several seconds of data, we present a neural network approach capable of reconstructing frequencies of several hundred Hertz within approximately 10 milliseconds. This enables rapid trigger generation. The method outperforms standard Fourier-based techniques, improving frequency estimation precision by a factor of 2 in the operational range of GINGERINO, our Ring Laser Gyroscope.\\ In addition to fast frequency estimation, we introduce an automated classification framework to identify physical disturbances in the signal, such as laser instabilities and seismic events, achieving accuracy rates between 99\% and 100\% on independent test datasets for the seismic class. These results mark a step forward in integrating artificial intelligence into signal analysis for geophysical applications.