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IEEE CDC 2020 Workshop

Compressed Sensing and Sparse Representation for Systems and Control


Sparsity is one of the major topics in machine learning and signal processing. Compressed sensing, also known as sparse representation, refers to the recovery of a high-dimensional but low-complexity vector (or signal) from a limited number of measurements. The notion of sparsity has also been attracting attention in control systems. In control systems, the sparsity in time is proposed for resource-aware control, such as event- (or self-) triggered control, where sensing and actuation is performed when needed. Also, optimal control called maximum hands-off control directly minimizes the time duration on which the control is active (i.e. L0 norm). Sparsity is also available for model reduction of control systems and networks.

In this workshop, we will review recent advances of sparsity methods in systems and control, and communications. We give lectures on

List of Speakers


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