Software Toolbox

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1. Recurrence plot and recurrence quantification analysis

After download the Matlab Toolbox of recurrence plot and recurrence quantification analysis, please run "Examples.m" for the demos.
Please also see my tutorials on "Nonlinear Dynamics, Recurrence Analysis, and Complex Networks"
Part I – Nonlinear dynamical systems and chaos
Part II – Recurrence analysis of complex systems
Part III – Multiscale recurrence analysis
Part IV – Recurrence and complex networks

2. Heterogeneous recurrence analysis

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

3. EKG animation and state space representation

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

4. EKG Ensembles and waterfall visualization

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

5. VCG animation with the colored speed

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

6. The generator of fractal surfaces or images

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

7. Multifractal analysis of 3D surfaces or 2D images

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

8. Simulation modeling of spatiotemporal dynamics on fractal surfaces

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

9. Whole heart modeling – spatiotemporal dynamics of electrical wave conduction and propagation

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

10. Cross recurrence plot (CRP) and cross recurrence quantification analysis (CRQA)

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

11. Virtual sensing network for statistical process monitoring

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.

12. Logistic map and bifurcation diagram

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.

13. Self-organizing network

Please download the Software Toolbox via the "Self-organizing Network" in the mathworks file exchange website. Each variable is represented as a node in the complex network. Nonlinear-coupling forces move these nodes to derive a self-organizing topology of the network. As such, variables are clustered into sub-network communities. The demo codes simulate and generate two clusters of variables, then demonstrate the codes with measure variable-to-variable pairwise distances. This can be replaced with the use of nonlinear coupling analysis to measure variable-to-variable interdependence structures (see Ref[2] for group variable selection).

If you find this demo useful, please cite the following sources:
[1] H. Yang and G. Liu, “Self-organized topology of recurrence-based complex networks,” Chaos, Vol. 23, No. 4, p. 043116, 2013, DOI: 10.1063/1.4829877G.
[2] Liu and H. Yang, "Self-organizing network for group variable selection and predictive modeling,” Annals of Operations Research, Vol. 263, No. 1, p. 119-140, 2017. DOI: 10.1007/s10479-017-2442-2
[3] Hui Yang (2024). Self-organizing Network (https://www.mathworks.com/matlabcentral/fileexchange/172685-self-organizing-network), MATLAB Central File Exchange. Retrieved September 13, 2024.

14. 3D Visualization of VCG Signals, Lead Vectors, Body Torso

Please download the Software Toolbox via the "3D Visualization of VCG Signals, Lead Vectors, Body Torso" in the mathworks file exchange website. This toolbox includes codes and the example to create an animated visualization of 3D VCG signals, lead vector directions, and body torso.

If you find this demo useful, please cite the following sources:
[1] H. Yang, "Multiscale recurrence quantification analysis of spatial Vectorcardiogram (VCG) signals," IEEE Transactions on Biomedical Engineering, Vol. 58, No. 2, p. 339-347, 2011, DOI: 10.1109/TBME.2010.2063704
[2] H. Yang, S. T. S. Bukkapatnam, and R. Komanduri, "Spatiotemporal representation of cardiac Vectorcardiogram (VCG) signals," Biomedical Engineering Online, Vol.11, No. 16, 2012, DOI: 10.1186/1475-925X-11-16
[3] H. Yang, C. Kan, G. Liu and Y. Chen, "Spatiotemporal differentiation of myocardial infarctions," IEEE Transactions on Automation Science and Engineering, Vol. 10, No. 4, p. 938-947, 2013, DOI: 10.1109/TASE.2013.2263497
[4] Hui Yang (2024). 3D Visualization of VCG Signals, Lead Vectors, Body Torso (https://www.mathworks.com/matlabcentral/fileexchange/172695-3d-visualization-of-vcg-signals-lead-vectors-body-torso), MATLAB Central File Exchange. Retrieved September 13, 2024.

15. Visualizing Melt Pool Dynamics in Metal-based Additive Manufacturing

Please download the Software Toolbox via the "Visualizing Melt Pool Dynamics in Metal-based Additive Manufacturing" in the mathworks file exchange website. This toolbox includes codes and the example to visualizes melt pools that are sensed and measured in the Laser powder bed fusion (LPBF) process. LPBF is a key technology of additive manufacturing that enables the fabrication of metal parts with complex geometry through a multi-layer process. Melt-pool morphological characteristics are eminent indicators for manufacturing process stability and part quality.

If you find this demo useful, please cite the following sources:
[1] Zhang, S., Yang, H. *, Yang, Z., & Lu, Y. (2024). Engineering-Guided Deep Learning of Melt-Pool Dynamics for Additive Manufacturing Quality Monitoring. Journal of Computing and Information Science in Engineering, 24(10). DOI: 10.1115/1.4066026
[2] Zhang, S., Lu, Y., & Yang, H. * (2024). Multiscale basis modeling of 3D melt-pool morphological variations for manufacturing process monitoring. The International Journal of Advanced Manufacturing Technology, 1-12. DOI: 10.1007/s00170-024-13377-2
[3] Hui Yang (2024). Additive Manufacturing - Visualizing Melt Pool Dynamics (https://www.mathworks.com/matlabcentral/fileexchange/173645-additive-manufacturing-visualizing-melt-pool-dynamics), MATLAB Central File Exchange. Retrieved October 8, 2024.

Author: Dr. Hui Yang is a Professor of Industrial and Manufacturing Engineering, Biomedical Engineering at Penn State, and is affiliated with Penn State Cancer Institute (PSCI), Clinical and Translational Science Institute (CTSI), Institute for Computational and Data Sciences (ICDS), CIMP-3D. Currently, he serves as the director of NSF Center for Health Organization Transformation (CHOT). Prior to joining Penn State in 2015, he was an Assistant Professor in the Department of Industrial and Management Systems Engineering at the University of South Florida from 2009 to 2015.

Dr. Yang's research interests focus on sensor-based modeling and analysis of complex systems for process monitoring, process control, system diagnostics, condition prognostics, quality improvement, and performance optimization. His research program is supported by National Science Foundation (including the prestigious NSF CAREER award), National Institute of Health (NIH), National Institute of Standards and Technology (NIST), MxD-The Digital Manufacturing and Cybersecurity Institute, CESMII-The Smart Manufacturing Institute, Fulbright Foundation, Lockheed Martin, IBM, GM, NSF center for e-Design, Susan Koman Foundation, Highmark, Siemens Healthineers, NSF Center for Healthcare Organization Transformation, Institute of Cyberscience, James A. Harley Veterans Hospital, and Florida James and Esther King Biomedical research program. His research group received a number of best paper awards and best poster awards from IISE, IEEE EMBC, IEEE CASE, and INFORMS Conferences.

Dr. Yang is the president (2017-2018) of IISE Data Analytics and Information Systems Society, the president (2015-2016) of INFORMS Quality, Statistics and Reliability (QSR) society, and the program chair of 2016 Industrial and Systems Engineering Research Conference (ISERC). He is also the Editor-in-Chief (EIC) for IISE Transactions Healthcare Systems Engineering, as well as an Associate Editor (AE) for IISE Transactions, IEEE Journal of Biomedical and Health Informatics (JBHI), ASME Journal of Computing and Information Science in Engineering (JCISE), IEEE Transactions on Automation Science and Engineering (TASE), IEEE Robotics and Automation Letters (RA-L), Quality Technology & Quantitative Management, and an Associate Editor for the Proceedings of IEEE CASE, IEEE EMBC, and IEEE BHI. He serves as a referee for a diverse set of top tier research journals such as Physical Review, IEEE Transactions on Biomedical Engineering, IEEE Journal of Biomedical and Health Informatics, Biophysical Journal, IIE Transactions, Technometrics, and IEEE Transactions on Automation Science and Engineering. He is a professional member of IEEE, IEEE EMBS, INFORMS, IIE, ASEE and American Heart Association (AHA).