After download the Toolbox of heterogeneous recurrence analysis, please run "Main_Lorenz_Demo.m" for the demos.

After download the Toolbox of EKG Animation and State State Representation, please run "EKG_Animation_gif.m" for the demos.

After download the Toolbox of EKG ensembles and waterfall visualization, please run "main_plotECGensemble_diffcycles.m" for the demos.

After download the Toolbox of VCG animation with the colored speed, please run "VCG_Animation_color_speed.m" for the demos.

After download the Toolbox of the generator of fractal surfaces or images, please run "demo_generate_simsurface.m" for the demos.

After download the Toolbox of multifractal analysis of 3D surfaces or 2D images, please run "demo_compute_multifractalspec.m" for the demos.

After download the Toolbox of spatiotemporal dynamics simulation on the fractal surface, please run "demo_dynamics_fractalsurfs.m" for the demos.

After download the Toolbox of spatiotemporal dynamics simulation in the whole heart, please run "demo_dynamics_wholeheart.m" for the demos.
Presentation slides:Click here for presentation slides
DOI:10.13140/RG.2.2.24336.51200

After download the CRP and CRQA Toolbox , please run "CRPExamplesSineWaves.m" for the demos.
If you find this demo useful, please cite the following papers:
[1] H. Yang, "Multiscale Recurrence Quantification Analysis of Spatial Vectorcardiogram (VCG) Signals," IEEE Transactions on Biomedical Engineering, Vol. 58, No. 2, p339-347, 2011 DOI: 10.1109/TBME.2010.2063704
[2] A. Meyers, M. Buqammaz, and H. Yang, "Cross Recurrence Analysis for Pattern Matching of Multidimensional Physiological Signals," Chaos: An Interdisciplinary Journal of Nonlinear Science (Feature Article), Vol. 30, No. 12, p.123125, DOI: 10.1063/5.0030838
Also available from: https://www.researchgate.net/publication/346926956_Cross_Recurrence_Analysis_for_Pattern_Matching_of_Multidimensional_Physiological_Signals

Please download the Software Toolbox via the Zenodo website.
If you find this demo useful, please cite the following sources:
[1] A. Krall, D. Finke, and H. Yang, "Virtual Sensing Network for Statistical Process Monitoring," IISE Transactions, 2022, p.1-30, DOI: 10.1080/24725854.2022.2148779
[2] Krall, Alexander, Finke, Dan, and Yang,Hui. (2022). Virtual Sensing Network for Statistical Process Monitoring. Software Package in Zenodo. https://doi.org/10.5281/zenodo.7311124

Illustration of VSN network model for sensor-based process monitoring. Physical sensors produce time-varying signals. Virtual sensors (shown in red) are placed within the signal space to produce a set of dynamic network profiles. These network profiles have their flux rank extracted and monitored for decision support.

Please download the Software Toolbox via the "Logistic map and bifurcation diagram" in the mathworks file exchange website. Logistic growth assumes that the growth rate is not constant but proportional to the remaining capacity, and describes the behavior of a population that has limited resources (food, water, space). Bifurcation Diagram depicts possible long-term values of a system as a function of a bifurcation parameter in the system. A small difference in the value of r or x0 can make a hugedifference in the outcome of the system at time t. No formula can tell us what x will be at some specified time t even if we know the initial conditions.

If you find this demo useful, please cite the following sources:
[1] Hui Yang (2023). “Mining Nonlinear Dynamics in Operational Data for Process Improvement”, INFORMS TutORials in Operations Research, pages 1-24. DOI: 10.1287/educ.2023.0261
[2] Hui Yang (2023). Logistic map and bifurcation diagram (https://www.mathworks.com/matlabcentral/fileexchange/135932-logistic-map-and-bifurcation-diagram), MATLAB Central File Exchange. Retrieved September 26, 2023.