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MathWorks Inc source matlab toolbox eeglab
Source Matlab Toolbox Eeglab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 93/100, based on 22 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/matlab+open+source+toolbox/MATLAB+Production+Server+Client+Libraries/pm32234478-295-15-16
Average 93 stars, based on 22 article reviews

source matlab toolbox eeglab - by Bioz Stars, 2026-10

93/100 stars

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Article Title: Exploiting Online Spatially Resolved Dynamic Light Scattering and Flow-NMR for Automated Size Targeting of PISA-Synthesized Block Copolymer Nanoparticles
Article Snippet: .. The instrument’s proprietary flow correction algorithms decoupled flow motion from diffusive motion in the phase resolved signal to yield final autocorrelation functions at each pathlength in Figure S9.1–3 A modified cumulants fitting method was used to obtain the cumulants Z-Average hydrodynamic diameter (d.nm) and a polydispersity index (PDI) for the expected monomodal distribution.4 A MATLAB™ Client interfaced with the instrument using the Open Platform Communications Unified Protocol (OPCUA) for real-time monitoring, data collection and instrument control over a network connection. ..

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Article Title: Teacups, a Python Package for the Simulation of Time-Resolved EPR Spectra of Spin-Polarized Multi-Spin Systems.
Article Snippet: A XXXX, XXX, XXX−XXX L (39) Pribitzer, S.; Doll, A.; Jeschke, G. SPIDYAN, a MATLAB library for simulating pulse EPR experiments with arbitrary waveform excitation.

Article Title: Comparative Study of Machine Learning and System Identification for Process Systems Engineering Dynamics
Article Snippet: All data-driven models were implemented using a mixture of Python and MATLAB libraries/toolboxes.

Article Title: The Fourier–Malliavin Volatility (FMVol) MATLAB® library
Article Snippet: To describe our MATLAB® library, e generate a single trajectory of a bi-variate version of the well-known stochastic volatility model by [13]: dx1(t) = ( μ1 − 1 2 v1(t) ) dt + √ v1(t)dW 1t (23) dx2(t) = ( μ2 − 1 2 v2(t) ) dt + √ v2(t)dW 2t (24) dv1(t) = θ1(α1 − v1(t))dt + γ1 √ v1(t)dW 3t (25) dv2(t) = θ2(α2 − v2(t))dt + γ2 √ v2(t)dW 4t (26) where W 1,W 2,W 3,W 4 are correlated Brownian motions such that ⟨dW kt , dW rt ⟩ = ρk,r dt.

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Article Title: Process simulation and evaluation of scaled-up biocatalytic systems: Advances, challenges, and future prospects.
Article Snippet: With the increased demand for bio-based products and the rapid development of biomanufacturing technologies, biocatalytic reactions including microorganisms and enzyme based, have become promising approaches.. Prior to the scale-up of production process, environmental and economic feasibility analysis are essential for the development of a sustainable and intelligent bioeconomy in the context of industry 4.0.. To achieve these goals, process simulation supports system optimization, improves energy and resource utilization efficiencies, and supports digital bioprocessing.

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