Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents efficient algorithms for the analysis of non-stationary multi-component signals based on modified local polynomial time frequency transform. The signals to be analyzed are divided into a number of segments and the desired parameters are estimated in each segment for computing modified local polynomial time frequency transform. Compared to other reported algorithms, the length of overlap beten consecutive segments is reduced to minimize the overall computational complexity. The concept of adaptive window lengths is also employed to achieve a better time-frequency resolution for each component.
['Yongmei Wei', 'Guoan Bi']
I am writing a research paper on a idea Efficient Algorithm for Modified Local Polynomial Time Frequency Transform
[{"from": "human", "value": "I am writing a research paper on a idea Efficient Algorithm for Modified Local Polynomial Time Frequency Transform"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents efficient algorithms for the analysis of non-stationary multi-component signals based on modified local polynomial time frequency transform. The signals to be analyzed are divided into a number of segments and the desired parameters are estimated in each segment for computing modified local polynomial time frequency transform. Compared to other reported algorithms, the length of overlap beten consecutive segments is reduced to minimize the overall computational complexity. The concept of adaptive window lengths is also employed to achieve a better time-frequency resolution for each component."}]