1. Books on Compressed Sensing, More Links #2 
   a. Foucart, Simon, et al. An invitation to compressive sensing. Springer New York, 2013.
   b. Majumdar, Angshul. Compressed sensing for engineers. CRC Press, 2018.
   c. Sankararajan, R., Rajendran, H., & Sukumaran, A. N. (2022). Compressive Sensing for Wireless Communication: Challenges and Opportunities.
   d. …
2. Books on Wavelets, [Link]More Links #1
   a. Heil, C. and Walnut, D.F. eds., 2006. Fundamental papers in wavelet theory. Princeton University Press.
   b. Kaiser, G. and Hudgins, L.H., 1994. A friendly guide to wavelets (Vol. 300). Boston: Birkhäuser.
   c. Newland, D.E., 2012. An introduction to random vibrations, spectral & wavelet analysis. Courier Corporation.
   d. Stephane, M. (1999). A wavelet tour of signal processing.
   e. Fugal, D.L., 2009. Conceptual wavelets in digital signal processing: an in-depth, practical approach for the non-mathematician. (No Title).
   f. Devlin, K. and Lorden, G., 2007. The numbers behind NUMB3RS: Solving crime with mathematics. Penguin.
   g. Weeks, M., 2010. Digital Signal Processing Using MATLAB & Wavelets added for testing purpose. Jones & Bartlett Publishers.
   h. Meyer, Y., 1993. Wavelets: algorithms & applications. Philadelphia: SIAM (Society for Industrial and Applied Mathematics.
   i. …

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To download the PDF files listed below, please go to “ More Links (2022 Books & Chapters)”

1. Interpretable Machine Learning  A Guide for Making Black Box Models Explainable (可解释机器学习–黑盒模型可解释性理解指南) 
2. Introduction to Statistics Introduction, examples and definitions
3. Moseley, B., 2022. Physics-informed machine learning: from concepts to real-world applications (Doctoral dissertation, University of Oxford). [Link, pdf]
4. 拿什么来拯救你的论文谈论文写作系列、科研中的苦与乐 、算法岗位找工作经验分享、科研人员如何高效工作并精彩生活。 
5. Stretcu, Otilia. Curriculum Learning. Diss. Carnegie Mellon University, 2021. 
6. Wu, Lingfei, et al. “Graph neural networks.” Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, Singapore, 2022. 27-37. [Link]
7. Wright, John, and Yi Ma. High-dimensional data analysis with low-dimensional models: Principles, computation, and applications. Cambridge University Press, 2022.  [Link]
8. Flandrin, Patrick. Explorations in time-frequency analysis. Cambridge University Press, 2018. 
9. Daubechies, Ingrid. Ten lectures on wavelets. Society for industrial and applied mathematics, 1992. 
10. Daubechies, Ingrid. Fundamental papers in wavelet theory. Princeton University Press, 2006. 
11. Debnath, Lokenath. Wavelet transforms and time-frequency signal analysis. Springer Science & Business Media, 2012. 
12. Nesterov, Yurii. Lectures on convex optimization. Vol. 137. Berlin: Springer International Publishing, 2018. [Link]
13. Nesterov, Yurii. Introductory lectures on convex optimization: A basic course. Vol. 87. Springer Science & Business Media, 2003. 
14. Nesterov, Yurii. “Introductory lectures on convex programming.” (1998). 
15. Jorge, Nocedal, and J. Wright Stephen. Numerical optimization. Spinger, 2006. 
16. Boyd, Stephen, Stephen P. Boyd, and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004. 
17. Randall, Robert Bond. Randall, Robert Bond. Vibration-based condition monitoring: industrial, automotive and aerospace applications. John Wiley & Sons, 202. John Wiley & Sons, 2011. 
18. Matplotlib: A scientific visualization toolbox
19. Guidelines for Research Planning-Ming J Zuo 

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