Addressing Problems of Embedded Systems via Compressive Sensing
Theory, Methodology and Application
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Compressive sensing is a mathematical theory concerning approximate recovery of sparse vectors using the minimum number of measurements called projections. Its theory covers topics such as sparse optimisation, dimensionality reduction, information preserving projection matrices, random projection matrices and others. In this book we extend and use the theory of compressive sensing to address the challenges of limited computation power and energy supply in embedded systems. The solutions of the problems in this book provide a good reference for the fellows. The related theories, methodologies a...
Compressive sensing is a mathematical theory concerning approximate recovery of sparse vectors using the minimum number of measurements called projections. Its theory covers topics such as sparse optimisation, dimensionality reduction, information preserving projection matrices, random projection matrices and others. In this book we extend and use the theory of compressive sensing to address the challenges of limited computation power and energy supply in embedded systems. The solutions of the problems in this book provide a good reference for the fellows. The related theories, methodologies and applications are discussed in detail. This should especially be useful for researchers or engineers in the field of Internet of Things (IoTs) aiming to implement the high performance applications on the resource constrained devices.
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