Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The recently introduced theory of compressive sensing enables the recovery of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of measurements can be much smaller than the number of Nyquist-rate samples. Interestingly, it has been shown that random projections are a near-optimal measurement scheme. This has inspired the design of hardware systems that directly implement random measurement protocols. Hover, despite the intense focus of the community on signal recovery, many (if not most) signal processing problems do not require full signal recovery. ,  take some first steps in the direction of solving inference problems-such as detection, classification, or estimation-and filtering problems using only compressive measurements and without ever reconstructing the signals involved.  provide theoretical bounds along with experimental results.
['Mark A. Davenport', 'Petros T. Boufounos', 'Michael B. Wakin', 'Richard G. Baraniuk']
I am writing a research paper on a idea Signal Processing With Compressive Measurements
[{"from": "human", "value": "I am writing a research paper on a idea Signal Processing With Compressive Measurements"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The recently introduced theory of compressive sensing enables the recovery of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of measurements can be much smaller than the number of Nyquist-rate samples. Interestingly, it has been shown that random projections are a near-optimal measurement scheme. This has inspired the design of hardware systems that directly implement random measurement protocols. Hover, despite the intense focus of the community on signal recovery, many (if not most) signal processing problems do not require full signal recovery. ,  take some first steps in the direction of solving inference problems-such as detection, classification, or estimation-and filtering problems using only compressive measurements and without ever reconstructing the signals involved.  provide theoretical bounds along with experimental results."}]