Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a new non-stationary signal classification algorithm based on a time-frequency distribution and multiple hypothesis testing. The time-frequency distribution is used to construct a time-dependent quadratic discriminant function. At selected points in time  evaluate the discriminant function and form a set of statistics which are used to test multiple hypotheses.  show that the statistics are a linear combinations of chi square random variables with constant coefficients and hence are not normally distributed. The multiple hypotheses are treated simultaneously using the generalised sequentially rejective Bonferroni test to control the probability of incorrect classification of one class.
['Geoff Roberts', 'Abdelhak M. Zoubir', 'Boualem Boashash']
I am writing a research paper on a idea Time-Frequency Discriminant Analysis for Non-Stationary Gaussian Signals
[{"from": "human", "value": "I am writing a research paper on a idea Time-Frequency Discriminant Analysis for Non-Stationary Gaussian Signals"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a new non-stationary signal classification algorithm based on a time-frequency distribution and multiple hypothesis testing. The time-frequency distribution is used to construct a time-dependent quadratic discriminant function. At selected points in time  evaluate the discriminant function and form a set of statistics which are used to test multiple hypotheses.  show that the statistics are a linear combinations of chi square random variables with constant coefficients and hence are not normally distributed. The multiple hypotheses are treated simultaneously using the generalised sequentially rejective Bonferroni test to control the probability of incorrect classification of one class."}]