The paper presents the design and evaluation of an adaptive signal processing procedure based on human skill. The focus is on interpreting probe signals detected in gas}liquid #ow in the presence of noise where existing signal interpretation techniques may encounter di$culties. Interpretation of a p
The genetic algorithm for a signal enhancement
β Scribed by L. Karimova; E. Kuadykov; N. Makarenko
- Publisher
- Elsevier Science
- Year
- 2004
- Tongue
- English
- Weight
- 219 KB
- Volume
- 534
- Category
- Article
- ISSN
- 0168-9002
No coin nor oath required. For personal study only.
β¦ Synopsis
The paper is devoted to the problem of time series enhancement, which is based on the analysis of local regularity. The model construction using this analysis does not require any a priori assumption on the structure of the noise and the functional relationship between original signal and noise. The signal itself may be nowhere differentiable with rapidly varying local regularity, what is overcome with the help of the new technique of increasing the local Ho¨lder regularity of the signal under research. A new signal with prescribed regularity is constructed using the genetic algorithm. This approach is applied to enhancement of time series in the paleoclimatology, solar physics, dendrochronology, meteorology and hydrology.
π SIMILAR VOLUMES
Image enhancement techniques are used to improve image quality or extract the fine details in the degraded images. Most existing color image enhancement techniques usually have three weaknesses: (1) color image enhancement applied in the RGB (red, green, blue) color space is inappropriate for the hu
characterized, and this is the reason for the poor return loss and the ripples in the response. For the electromagnetic analysis of the CPW discontinu-Ε½ .w x ities, em Sonnet 10 has been used. The measurement was made using a Cascade Microtech wafer probe station and an HP8510B vector analyzer. As
Assembly is a key manufacturing process for many industries and the use of Design for Assembly (DFA) can have substantial benefits. This paper examines the DFA problem, especially in the context of global manufacturing where suppliers may be distributed over the globe. The use of Genetic Algorithms