Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The problem of recovering complex signals from low quality sensing devices is analyzed via a combination of tools from signal processing and harmonic analysis. By using the rich structure offered by the recent development in fusion frames,  introduce a framework for splitting the complex information into local pieces. Each piece of information is measured by potentially very low quality sensors, modeled by linear matrices and recovered via compressed sensing -- when necessary. Finally, by a fusion process within the fusion frames,  are able to recover accurately the original high-dimensional signal. #R##N#Using our new method,  show, and illustrate on simple numerical examples, that it is possible, and sometimes necessary, to split a signal via local projections for accurate, stable, and robust estimation. In particular,  show that by increasing the size of the fusion frame,  encourage a certain robustness to noise. While the complexity remains relatively low,  achieve stronger recovery performance compared to usual single-device compressed sensing.
['Roza Aceska', 'Jean-Luc Bouchot', 'Shidong Li']
I am writing a research paper on a idea Local sparsity and recovery of fusion frames structured signals
[{"from": "human", "value": "I am writing a research paper on a idea Local sparsity and recovery of fusion frames structured signals"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The problem of recovering complex signals from low quality sensing devices is analyzed via a combination of tools from signal processing and harmonic analysis. By using the rich structure offered by the recent development in fusion frames,  introduce a framework for splitting the complex information into local pieces. Each piece of information is measured by potentially very low quality sensors, modeled by linear matrices and recovered via compressed sensing -- when necessary. Finally, by a fusion process within the fusion frames,  are able to recover accurately the original high-dimensional signal. #R##N#Using our new method,  show, and illustrate on simple numerical examples, that it is possible, and sometimes necessary, to split a signal via local projections for accurate, stable, and robust estimation. In particular,  show that by increasing the size of the fusion frame,  encourage a certain robustness to noise. While the complexity remains relatively low,  achieve stronger recovery performance compared to usual single-device compressed sensing."}]