Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An algorithm for obtaining nonnegative, joint time-frequency distributions Q(t, f) satisfying the univariate marginals |s(t)|/sup 2/ and |S(f)|/sup 2/ is presented and applied. The advantage of the algorithm is that large time series records can be processed without the need for large random access memory (RAM) and central processing unit (CPU) time. This algorithm is based on the Loughlin et al. (1992) method for synthesizing positive distributions using the principle of minimum cross-entropy. The nonnegative distributions with the correct marginals that are obtained using this approach are density functions as proposed by Cohen and Zaparovanny (1980) and Cohen and Posch (1985). Three examples are presented: the first is a nonlinear frequency modulation (FM) sep signal (simulated data); the second and third are of physical systems (real data). The second example is the signal for the acoustic scattering response of an elastic cylindrical shell structure. The third example is of an acoustic transient signal from an underwater vehicle. Example one contains 7500 data points, example two contains 256 data points, and example three contains in excess of 30000 data points. The RAM requirements using the original Loughlin et al. algorithm for a 7500 data point signal is 240 mega bytes and for a 30000 data point signal is 3.5 billion bytes. The new algorithm reduces the 240 mega byte requirement to 1 mega byte and the 3.5 billion byte requirement to 4 million bytes. Furthermore, the fast algorithm runs 240 times faster for the 7500 data point signal and 3000 times faster for the 30000 data point signal as compared with the original Loughlin et al. algorithm.
['Dale Groutage']
I am writing a research paper on a idea A fast algorithm for computing minimum cross-entropy positive time-frequency distributions
[{"from": "human", "value": "I am writing a research paper on a idea A fast algorithm for computing minimum cross-entropy positive time-frequency distributions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An algorithm for obtaining nonnegative, joint time-frequency distributions Q(t, f) satisfying the univariate marginals |s(t)|/sup 2/ and |S(f)|/sup 2/ is presented and applied. The advantage of the algorithm is that large time series records can be processed without the need for large random access memory (RAM) and central processing unit (CPU) time. This algorithm is based on the Loughlin et al. (1992) method for synthesizing positive distributions using the principle of minimum cross-entropy. The nonnegative distributions with the correct marginals that are obtained using this approach are density functions as proposed by Cohen and Zaparovanny (1980) and Cohen and Posch (1985). Three examples are presented: the first is a nonlinear frequency modulation (FM) sep signal (simulated data); the second and third are of physical systems (real data). The second example is the signal for the acoustic scattering response of an elastic cylindrical shell structure. The third example is of an acoustic transient signal from an underwater vehicle. Example one contains 7500 data points, example two contains 256 data points, and example three contains in excess of 30000 data points. The RAM requirements using the original Loughlin et al. algorithm for a 7500 data point signal is 240 mega bytes and for a 30000 data point signal is 3.5 billion bytes. The new algorithm reduces the 240 mega byte requirement to 1 mega byte and the 3.5 billion byte requirement to 4 million bytes. Furthermore, the fast algorithm runs 240 times faster for the 7500 data point signal and 3000 times faster for the 30000 data point signal as compared with the original Loughlin et al. algorithm."}]