Fast, Exact Synthesis of Gaussian and nonGaussian Long-Range-Dependent Processes
| dc.contributor.author | Baraniuk, Richard | en_US |
| dc.contributor.author | Crouse, Matthew | en_US |
| dc.date.accessioned | 2009-04-15T16:59:06Z | en_US |
| dc.date.available | 2009-04-15T16:59:06Z | en_US |
| dc.date.issued | 2009-04-15 | en_US |
| dc.description | Originally submitted to IEEE Transactions on Information Theory, August 1999. | en_US |
| dc.description.abstract | 1/f noise and statistically self-similar random processes such as fractional Brownian motion (fBm) and fractional Gaussian noise (fGn) are fundamental models for a host of real-world phenomena, from network traffic to DNA to the stock market. Synthesis algorithms play a key role by providing the feedstock of data necessary for running complex simulations and accurately evaluating analysis techniques. Unfortunately, current algorithms to correctly synthesize these long-range dependent (LRD) processes are either abstruse or prohibitively costly, which has spurred the wide use of inexact approximations. To fill the gap, we develop a simple, fast (O(N logN) operations for a length-N signal) framework for exactly synthesizing a range of Gaussian and nonGaussian LRD processes. As a bonus, we introduce and study a new bi-scaling fBm process featuring a "kinked" correlation function that exhibits distinct scaling laws at coarse and fine scales. | en_US |
| dc.description.sponsorship | National Science Foundation, grant no. MIP–9457438, the Office of Naval Research, grant no. N00014–99–1–0813, by DARPA/AFOSR, grant no. F49620-97-1-0513, and by the Texas Instruments Leadership University Program | en_US |
| dc.identifier.citation | R. Baraniuk and M. Crouse, "Fast, Exact Synthesis of Gaussian and nonGaussian Long-Range-Dependent Processes," 2009. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/21941 | en_US |
| dc.language.iso | eng | en_US |
| dc.relation.IsPartOfSeries | Rice ECE Department Technical Report TREE9913 | en_US |
| dc.subject | fractional Brownian motion | en_US |
| dc.subject | FFT | en_US |
| dc.subject | random process | en_US |
| dc.subject | fractional Gaussian noise | en_US |
| dc.subject | synthesis | en_US |
| dc.title | Fast, Exact Synthesis of Gaussian and nonGaussian Long-Range-Dependent Processes | en_US |
| dc.type | Report | en_US |
| dc.type.dcmi | Text | en_US |