Awareness of the level of river flow and its fluctuations at different times is one of the significant factor to achieve sustainable development for water resource issues. Therefore, the present study two hybrid models, Wavelet- Adaptive Neural Fuzzy Interference System (WANFIS) and Wavelet- Artificial Neural Network (WANN) are used for flow prediction of Gamasyab River (Nahavand, Hamedan, Iran...

In this report, we discuss results of modelling and forecasting nonstationary financial time series using a combination of the maximal overlap discreet wavelet transform (MODWT) and fuzzy logic. A financial time series is decomposed into an over complete, shift invariant wavelet representation. A fuzzy-rule base is created for each individual wavelet sub-series to predict future values. To form...

In this correspondence I propose an image super-resolution technique based on interpolation of high frequency sub-band images obtained by Discrete Wavelet Transform on input image. In those sub-bands edges are enhanced by introducing an intermediate stage by using Stationary Wavelet Transform. The wavelet transform is applied in order to decompose image into different subbands. In those sub-ban...

Awareness of the level of river flow and its fluctuations at different times is one of the significant factor to achieve sustainable development for water resource issues. Therefore, the present study two hybrid models, Wavelet- Adaptive Neural Fuzzy Interference System (WANFIS) and Wavelet- Artificial Neural Network (WANN) are used for flow prediction of Gamasyab River (Nahavand, Hamedan, Iran...

The occurrence of cycle slips is a major limiting factor for achievement of sub-decimeter accuracy in positioning with GPS (Global Positioning System). In the past, several authors introduced a method based on different combinations of GPS data together with Kalman filter to solve the problem of the cycle slips. In this paper the same philosophy is used but with discrete wavelet transforms. For...

Wavelet thresholding is a signal estimation technique that exploits the capabilities of wavelet transform for signal denoising. But the choice of thresholding function has restricted there wide spread use in image denoising application. In this paper we proposed a computationally more efficient thresholding scheme by incorporating the neighbouring wavelet coefficients, with different threshold ...

It is well-known that the Shannon Sampling Theorem allows us to fully recover a continuous-time bandlimited signal from its digital samples, as long as the sampling rate to be chosen is not smaller than the Nyquist frequency. This theory applies to all bandlimited signals, which may or may not occupy the entire frequency band. Hence, it is intuitively convincing that for continuous-time signals...

Removing noise from images is a challenging problem in digital image processing. This paper presents an image denoising method based on a maximum a posteriori (MAP) density function estimator, which is implemented in the wavelet domain because of its energy compaction property. The performance of the MAP estimator depends on the proposed model for noise-free wavelet coefficients. Thus in the wa...

-Based on the statistical characters of remote sensing image, this paper proposed firstly partition remote sensing image into sub-block images of suitable sizes, and then decomposing these sub-block images with biorthogonal 7/5 wavelet. A novel construction method of wavelet set of coefficients is adopted to embed binary watermarking image into sub-bands except low frequency sub-bands, and real...

This paper describes an approach for accomplishing sub-octave wavelet analysis and its discrete implementation for noise reduction and feature enhancement. Sub-octave wavelet transforms allow us to more closely characterize features within distinct frequency bands. By dividing each octave into sub-octave components, we demonstrate a superior ability to capture transient activities in a signal o...