As a paradigm to recover the sparse signal from a small set of linear measurements,
compressed sensing (CS) has stimulated a great deal of interest in recent years. In order to …

压缩感知是建立在矩阵分析, 统计概率论, 拓扑几何, 优化与运筹学, 泛函分析等基础上的一种
全新的信息获取与处理的理论框架. 它基于信号的可压缩性, 通过低维空间, 低分辨率, 欠Nyquist …

Data-driven science and technology offer transformative tools and methods to science. This
review article highlights the latest development and progress in the interdisciplinary field of …

The lack of reliable methods for identifying descriptors—the sets of parameters capturing the
underlying mechanisms of a material's property—is one of the key factors hindering efficient …

We present a new image reconstruction method that replaces the projector in a projected
gradient descent (PGD) with a convolutional neural network (CNN). Recently, CNNs trained …

Optimal sensor and actuator placement is an important unsolved problem in control theory.
Nearly every downstream control decision is affected by these sensor and actuator …

This first chapter formulates the objectives of compressive sensing. It introduces the
standard compressive problem studied throughout the book and reveals its ubiquity in many …

We propose a sampling theory for signals that are supported on either directed or undirected
graphs. The theory follows the same paradigm as classical sampling theory. We show that …

Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in
electrical engineering, applied mathematics, statistics and computer science. This book …

A large number of vision applications rely on matching keypoints across images. The last
decade featured an arms-race towards faster and more robust keypoints and association …