English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We introduce a rapid and precise analytical approach for analyzing cerebralblood flow (CBF) using Diffuse Correlation Spectroscopy (DCS) with theapplication of the Extreme Learning Machine (ELM). Our evaluation of ELM andexisting algorithms involves a comprehensive set of metrics. We assess thesealgorithms using synthetic datasets for both semi-infinite and multi-layermodels. The results demonstrate that ELM consistently achieves higher fidelityacross various noise levels and optical parameters, showcasing robustgeneralization ability and outperforming iterative fitting algorithms. Througha comparison with a computationally efficient neural network, ELM attainscomparable accuracy with reduced training and inference times. Notably, theabsence of a back-propagation process in ELM during training results insignificantly faster training speeds compared to existing neural networkapproaches. This proposed strategy holds promise for edge computingapplications with online training capabilities.

Fast Cerebral Blood Flow Analysis via Extreme Learning Machine