Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel framework of fast and efficient compressive sampling based on the new concept of structurally random matrices. The proposed framework provides four important features, (i) It is universal with a variety of sparse signals, (ii) The number of measurements required for exact reconstruction is nearly optimal, (iii) It has very low complexity and fast computation based on block processing and linear filtering, (iv) It is developed on the provable mathematical model from which  are able to quantify trade-offs among streaming capability, computation/memory requirement and quality of reconstruction. All currently existing methods only have at most three out of these four highly desired features. Simulation results with several interesting structurally random matrices under various practical settings are also presented to verify the validity of the theory as ll as to illustrate the promising potential of the proposed framework.
['Thong T. Do', 'Trac D. Tran', 'Lu Gan']
I am writing a research paper on a idea Fast compressive sampling with structurally random matrices
[{"from": "human", "value": "I am writing a research paper on a idea Fast compressive sampling with structurally random matrices"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel framework of fast and efficient compressive sampling based on the new concept of structurally random matrices. The proposed framework provides four important features, (i) It is universal with a variety of sparse signals, (ii) The number of measurements required for exact reconstruction is nearly optimal, (iii) It has very low complexity and fast computation based on block processing and linear filtering, (iv) It is developed on the provable mathematical model from which  are able to quantify trade-offs among streaming capability, computation/memory requirement and quality of reconstruction. All currently existing methods only have at most three out of these four highly desired features. Simulation results with several interesting structurally random matrices under various practical settings are also presented to verify the validity of the theory as ll as to illustrate the promising potential of the proposed framework."}]