Sampling Theory, a Renaissance: Compressive Sensing and Other Developments

Part I: Sparsity Models.- Estimation in High Dimensions: A Geometric Perspective.- Convex Recovery of a Structured Signal from Independent Random Linear Measurements.- Low Complexity Regularization of Linear Inverse Problems.- Part II: Frames with Benefits.- Noise-shaping Quantization Methods for Frame-based and Compressive Sampling Systems.- Fourier Operations in Applied Harmonic Analysis.- The Fundamentals of Spectral Tetris Frame Constructions.- Part III: Bandlimitation Recast.- System Approximation and Generalized Measurements in Modern Sampling Theory.- Entire Functions in Generalized Bernstein Spaces and Their Growth Behavior.- Sampling and Geometry.- A Sheaf-theoretic Perspective on Sampling.- Part IV: Solutions of Parametric PDEs.- How to Best Sample a Solution Manifold?.- On the Stability of Polynomial Interpolation using Hierarchical Sampling.- Part V: Implementation.- OperA: Operator-based Annihilation for Finite-Rate-of-Innovation Signal Sampling.- Digital Adaptive Calibration of Data Converters using Independent Component Analysis.