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Explores tessellation for SMLM data analysis, covering Voronoi segmentation and colocalization principles, applications in various fields, and integration of quantification methods.
Covers free meshing algorithms, partitioning, and incompatible meshes in 3D simulations, emphasizing the importance of mesh quality and element compatibility.
Covers the basics of Machine Learning, including recognizing hand-written digits, supervised classification, decision boundaries, and polynomial curve fitting.
Covers the system model, linear receivers, performance evaluation, and graphical interpretations of MIMO receivers, including maximum likelihood detection and successive interference cancellation.
Covers quantization of probability distributions, statistical k-means clustering, mean estimation, robust clustering methods, and open research questions.
