#wavelets — Public Fediverse posts

Ah, yet another riveting #bedtime story about the thrilling escapades of #wavelets on #graphs 🤓✨. Because nothing screams excitement like spectral graph theory from 2009, now with 100% more arXiv-nerdery! 🔍📚💤
https://arxiv.org/abs/0912.3848 #spectraltheory #arXiv #nerdy #stories #HackerNews #ngated

Ah, yet another riveting #bedtime story about the thrilling escapades of #wavelets on #graphs 🤓✨. Because nothing screams excitement like spectral graph theory from 2009, now with 100% more arXiv-nerdery! 🔍📚💤
https://arxiv.org/abs/0912.3848 #spectraltheory #arXiv #nerdy #stories #HackerNews #ngated

Ah, yet another riveting #bedtime story about the thrilling escapades of #wavelets on #graphs 🤓✨. Because nothing screams excitement like spectral graph theory from 2009, now with 100% more arXiv-nerdery! 🔍📚💤
https://arxiv.org/abs/0912.3848 #spectraltheory #arXiv #nerdy #stories #HackerNews #ngated

Ah, yet another riveting #bedtime story about the thrilling escapades of #wavelets on #graphs 🤓✨. Because nothing screams excitement like spectral graph theory from 2009, now with 100% more arXiv-nerdery! 🔍📚💤
https://arxiv.org/abs/0912.3848 #spectraltheory #arXiv #nerdy #stories #HackerNews #ngated

Wavelets on Graphs via Spectral Graph Theory (2009)

https://arxiv.org/abs/0912.3848

#HackerNews #Wavelets #Graphs #SpectralGraphTheory #Research #2009

☕ Here's a bit of technical content from me - today a deep dive on #baseline correction methods.

📈 Baseline correction is a preprocessing technique to remove background signal and isolate peaks in hashtag#spectroscopy data.

📝 In my recent post I discuss two methods:
1. Wavelet transform (WT) - Decomposes signal into components at different frequencies. Lowest frequency component represents baseline and can be removed.
2. Asymmetric least squares (ALS) - Fits a smooth baseline function, penalising positive deviations more than negative ones.

TL;DR: WT method is intuitive but can distort peaks. ALS produces better results.

🔎 Both methods are applied on a #Raman spectrum and an X-ray fluorescence (#XRF) spectrum. ALS gives a cleaner baseline correction and it's effective for removing broad, slowly varying background while preserving sharper spectral features.

#chemometrics #Python #MachineLearning #wavelets #regression

https://nirpyresearch.com/two-methods-baseline-correction-spectral-data/

☕ Here's a bit of technical content from me - today a deep dive on #baseline correction methods.

📈 Baseline correction is a preprocessing technique to remove background signal and isolate peaks in hashtag#spectroscopy data.

📝 In my recent post I discuss two methods:
1. Wavelet transform (WT) - Decomposes signal into components at different frequencies. Lowest frequency component represents baseline and can be removed.
2. Asymmetric least squares (ALS) - Fits a smooth baseline function, penalising positive deviations more than negative ones.

TL;DR: WT method is intuitive but can distort peaks. ALS produces better results.

🔎 Both methods are applied on a #Raman spectrum and an X-ray fluorescence (#XRF) spectrum. ALS gives a cleaner baseline correction and it's effective for removing broad, slowly varying background while preserving sharper spectral features.

#chemometrics #Python #MachineLearning #wavelets #regression

https://nirpyresearch.com/two-methods-baseline-correction-spectral-data/

☕ Here's a bit of technical content from me - today a deep dive on #baseline correction methods.

📈 Baseline correction is a preprocessing technique to remove background signal and isolate peaks in hashtag#spectroscopy data.

📝 In my recent post I discuss two methods:
1. Wavelet transform (WT) - Decomposes signal into components at different frequencies. Lowest frequency component represents baseline and can be removed.
2. Asymmetric least squares (ALS) - Fits a smooth baseline function, penalising positive deviations more than negative ones.

TL;DR: WT method is intuitive but can distort peaks. ALS produces better results.

🔎 Both methods are applied on a #Raman spectrum and an X-ray fluorescence (#XRF) spectrum. ALS gives a cleaner baseline correction and it's effective for removing broad, slowly varying background while preserving sharper spectral features.

#chemometrics #Python #MachineLearning #wavelets #regression

https://nirpyresearch.com/two-methods-baseline-correction-spectral-data/

☕ Here's a bit of technical content from me - today a deep dive on #baseline correction methods.

📈 Baseline correction is a preprocessing technique to remove background signal and isolate peaks in hashtag#spectroscopy data.

📝 In my recent post I discuss two methods:
1. Wavelet transform (WT) - Decomposes signal into components at different frequencies. Lowest frequency component represents baseline and can be removed.
2. Asymmetric least squares (ALS) - Fits a smooth baseline function, penalising positive deviations more than negative ones.

TL;DR: WT method is intuitive but can distort peaks. ALS produces better results.

🔎 Both methods are applied on a #Raman spectrum and an X-ray fluorescence (#XRF) spectrum. ALS gives a cleaner baseline correction and it's effective for removing broad, slowly varying background while preserving sharper spectral features.

#chemometrics #Python #MachineLearning #wavelets #regression

https://nirpyresearch.com/two-methods-baseline-correction-spectral-data/

☕ Here's a bit of technical content from me - today a deep dive on #baseline correction methods.

📈 Baseline correction is a preprocessing technique to remove background signal and isolate peaks in hashtag#spectroscopy data.

📝 In my recent post I discuss two methods:
1. Wavelet transform (WT) - Decomposes signal into components at different frequencies. Lowest frequency component represents baseline and can be removed.
2. Asymmetric least squares (ALS) - Fits a smooth baseline function, penalising positive deviations more than negative ones.

TL;DR: WT method is intuitive but can distort peaks. ALS produces better results.

🔎 Both methods are applied on a #Raman spectrum and an X-ray fluorescence (#XRF) spectrum. ALS gives a cleaner baseline correction and it's effective for removing broad, slowly varying background while preserving sharper spectral features.

#chemometrics #Python #MachineLearning #wavelets #regression

https://nirpyresearch.com/two-methods-baseline-correction-spectral-data/

Wavelet decomposition is very popular in image analysis and processing. Here's my attempt to share some code to perform spectral smoothing (or denoising, to be fancy) using the same principle.

Wavelet denoising of spectra • NIRPY Research
https://nirpyresearch.com/wavelet-denoising-spectra/

#ImageProcessing #chemometrics #spectroscopy #wavelets #DataProcessing

Wavelet decomposition is very popular in image analysis and processing. Here's my attempt to share some code to perform spectral smoothing (or denoising, to be fancy) using the same principle.

Wavelet denoising of spectra • NIRPY Research
https://nirpyresearch.com/wavelet-denoising-spectra/

#ImageProcessing #chemometrics #spectroscopy #wavelets #DataProcessing

Can the Continuous Wavelet Transform (CWT) improve the predictions of your deep / machine learning models?

Reduced chance of over-fitting to noise, or other anomalies, in your raw data. Resulting in simpler lightweight models.

A powerful preprocessing technique.

https://medium.com/mlearning-ai/the-power-of-the-continuous-wavelet-transform-cwt-in-machine-learning-980e80ecfb8a

#wavelets #wavelet #cwt #dwt #MachineLearning #deeplearning #datascience #NeuralNetworks #preprocessing