Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider the problem of approximating a family of isocontours in a sensor fleld with a topologically-equivalent family of simple polygons. Our algorithm is simple and distributed, it gracefully adapts to any user-specified representation size  k , and it delivers a worst-case guarantee for the quality of approximation. In particular,  prove that the topology-respecting Hausdorff error in our  k  -vertex approximation is within a small constant factor of the optimal error possible with Θ( k /log  m ) vertices, where  m  is the number of contours. Evaluation of the algorithm on real data suggests that the size increase factor in practice is a constant near 2 .6, and shows  no  error increase. Our simulation results using a variety of synthetic and real data show that the algorithm smoothly handles complex isocontours, even for representation sizes as small as 32 or 48. Because isocontours are widely used to represent and communicate bi-variate signals, our technique is broadly applicable to innetwork aggregation and summarization of spatial data in sensor networks.
['Sorabh Gandhi', 'John Hershberger', 'Subhash Suri']
I am writing a research paper on a idea Approximate isocontours and spatial summaries for sensor networks
[{"from": "human", "value": "I am writing a research paper on a idea Approximate isocontours and spatial summaries for sensor networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider the problem of approximating a family of isocontours in a sensor fleld with a topologically-equivalent family of simple polygons. Our algorithm is simple and distributed, it gracefully adapts to any user-specified representation size  k , and it delivers a worst-case guarantee for the quality of approximation. In particular,  prove that the topology-respecting Hausdorff error in our  k  -vertex approximation is within a small constant factor of the optimal error possible with \u0398( k /log  m ) vertices, where  m  is the number of contours. Evaluation of the algorithm on real data suggests that the size increase factor in practice is a constant near 2 .6, and shows  no  error increase. Our simulation results using a variety of synthetic and real data show that the algorithm smoothly handles complex isocontours, even for representation sizes as small as 32 or 48. Because isocontours are widely used to represent and communicate bi-variate signals, our technique is broadly applicable to innetwork aggregation and summarization of spatial data in sensor networks."}]