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matlab tool (2017b)  (MathWorks Inc)


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    MathWorks Inc matlab tool (2017b)
    Matlab Tool (2017b), 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+tool+(2017b)/MATLAB+Production+Server+Client+Libraries/pm32869364-43-96-99
    Average 93 stars, based on 22 article reviews
    matlab tool (2017b) - by Bioz Stars, 2026-10
    93/100 stars

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    Related Articles

    Modification:

    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. ..

    Control:

    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. ..

    other:

    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.

    Transmission Assay:

    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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    The discriminability of responses estimated using a SOM neural net. The spatio-temporally patterned array of inputs is weighted based on unsupervised learning to cluster the sets of inputs according to the variability and the patterns present in the dataset. Large intrinsic differences in patterns between responses to 2 stimuli thus lead to reliable clustering. We compare this SOM decoder where each timed point and each neuron are weighted independently (magenta) to a decoder based on Euclidean distance with time points and neurons kept as separate dimensions or a van Rossum metric where responses of different neurons are averaged together before the comparison. (A) PC neuron responses to electrosensory chirps. (B) PN response of the moth antennal lobe to odors. (C) LIF model responses to frozen white noise stimuli of different shapes. Curves show averages (± s.d.) across all pairs of stimuli (number of stimuli: electrosensory = 3 different chirps, 3 pairs; olfactory = 7 odors, 21 pairs; LIF = 10 noise patterns, 45 pairs).

    Journal: Frontiers in Neuroscience

    Article Title: Characterizing neural coding performance for populations of sensory neurons: comparing a weighted spike distance metrics to other analytical methods

    doi: 10.3389/fnins.2023.1175629

    Figure Lengend Snippet: The discriminability of responses estimated using a SOM neural net. The spatio-temporally patterned array of inputs is weighted based on unsupervised learning to cluster the sets of inputs according to the variability and the patterns present in the dataset. Large intrinsic differences in patterns between responses to 2 stimuli thus lead to reliable clustering. We compare this SOM decoder where each timed point and each neuron are weighted independently (magenta) to a decoder based on Euclidean distance with time points and neurons kept as separate dimensions or a van Rossum metric where responses of different neurons are averaged together before the comparison. (A) PC neuron responses to electrosensory chirps. (B) PN response of the moth antennal lobe to odors. (C) LIF model responses to frozen white noise stimuli of different shapes. Curves show averages (± s.d.) across all pairs of stimuli (number of stimuli: electrosensory = 3 different chirps, 3 pairs; olfactory = 7 odors, 21 pairs; LIF = 10 noise patterns, 45 pairs).

    Article Snippet: We used the SOM neural net tool built into Matlab 2017b (Mathworks, Natick, MA) and detailed documentation can be obtained from the provider.

    Techniques:

    A modified Euclidean distance, where each dimension is weighted, allows accurate discrimination with similar -or better- performance than SOM neural nets. In the “WED” (Weighted Euclidean Distance) analysis, each dimension in Euclidean space is weighted based on the Kullback–Leibler divergence of the response distribution in that dimension. Each dimension (neuron/time bin) can be weighted independently (‘independent W’), or a single weight can be set for a given neuron across time bins (‘fixed W’). Although using independent weights maximizes the information extracted about the difference in stimuli, using a fixed weight emulates a biologically more realistic decoding network. The best method varies across systems: (A) Electrosensory; (B) Olfactory; (C) LIF model. Curves show averages (± s.d.) across all pairs of stimuli.

    Journal: Frontiers in Neuroscience

    Article Title: Characterizing neural coding performance for populations of sensory neurons: comparing a weighted spike distance metrics to other analytical methods

    doi: 10.3389/fnins.2023.1175629

    Figure Lengend Snippet: A modified Euclidean distance, where each dimension is weighted, allows accurate discrimination with similar -or better- performance than SOM neural nets. In the “WED” (Weighted Euclidean Distance) analysis, each dimension in Euclidean space is weighted based on the Kullback–Leibler divergence of the response distribution in that dimension. Each dimension (neuron/time bin) can be weighted independently (‘independent W’), or a single weight can be set for a given neuron across time bins (‘fixed W’). Although using independent weights maximizes the information extracted about the difference in stimuli, using a fixed weight emulates a biologically more realistic decoding network. The best method varies across systems: (A) Electrosensory; (B) Olfactory; (C) LIF model. Curves show averages (± s.d.) across all pairs of stimuli.

    Article Snippet: We used the SOM neural net tool built into Matlab 2017b (Mathworks, Natick, MA) and detailed documentation can be obtained from the provider.

    Techniques: Modification