The dogma of signal processing maintains that a signal must be sampled at a rate at least twice its highest frequency in order to be represented without error. However, in practice, we often compress the data soon after sensing, trading off signal representation complexity (bits) for some error (consider JPEG image compression in digital cameras, for example). Clearly, this is wasteful of valuable sensing resources. Over the past few years, a new theory of "compressive sensing" has begun to emerge, in which the signal is sampled (and simultaneously compressed) at a greatly reduced rate.

As the compressive sensing research community continues to expand rapidly, it behooves us to heed Shannon's advice.

Compressive sensing is also referred to in the literature by the terms: compressed sensing, compressive sampling, and sketching/heavy-hitters.

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Tutorials and Reviews
Compressive Sensing
Extensions of Compressive Sensing
Multi-Sensor and Distributed Compressive Sensing
Model-based Compressive Sensing
Compressive Sensing and Quantization
Compressive Sensing Recovery Algorithms
Foundations and Connections
Coding and Information Theory
High-Dimensional Geometry
Ell-1 Norm Minimization
Statistical Signal Processing
Machine Learning
Bayesian Methods
Finite Rate of Innovation
Adaptive Sampling Methods for Sparse Recovery
Data Stream Algorithms
Random Sampling
Histogram Maintenance
Dimension Reduction and Embeddings
Applications of Compressive Sensing
Compressive Imaging
Medical Imaging
Analog-to-Information Conversion
Computational Biology
Geophysical Data Analysis
Hyperspectral Imaging
Compressive Radar
Compressive System Identification and Dynamical Systems
Surface Metrology
Acoustics, Audio, and Speech Processing
Remote Sensing
Computer Engineering
Computer Graphics
Robotics & Control
Content Based Retrieval
Conferences and Workshops

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