Topic
Discrete sine transform
About: Discrete sine transform is a research topic. Over the lifetime, 3269 publications have been published within this topic receiving 73181 citations.
Papers published on a yearly basis
Papers
More filters
Patent•
22 Sep 1988
TL;DR: In this article, a picture encoding system which performs a prediction of picture element values within a block having a plurality of picture elements into which an original picture is divided, is presented.
Abstract: A picture encoding system which performs a prediction of picture element values within a block having a plurality of picture elements into which an original picture is divided, a discrete sine transform with respect to prediction error signals for obtaining a transform coefficient, a quantization of the transform coefficient for encoding quantized indexes, an inverse quantization of the quantized indexes for reproduction of the transform coefficient, an inverse discrete sine transform of the reproduced coefficient to reproduce the prediction error signal, and an addition thereto of the predicted picture element value for reproducing the picture element values within the block to employ them in predicting a next stage block to be encoded, whereby the block can be minimized in size while simplifying required transform operation.
78 citations
01 Jan 1969
TL;DR: In this paper, the Hilbert transform relations, as they apply to sequences and their z-transforms, and also as the number of data samples taken in the Discrete Fourier Transforms becomes infinite, are discussed.
Abstract: The Hilbert transform has traditionally played an important part in the theory and practice of signal processing operations in continuous system theory because of its relevance to such problems as envelope detection and demodulation, as well as its use in relating the real and imaginary components, and the magnitude and phase components of spectra. The Hilbert transform plays a similar role in digital signal processing. In this paper, the Hilbert transform relations, as they apply to sequences and their z-transforms, and also as they apply to sequences and their Discrete Fourier Transforms, will be discussed. These relations are identical only in the limit as the number of data samples taken in the Discrete Fourier Transforms becomes infinite. The implementation of the Hilbert transform operation as applied to sequences usually takes the form of digital linear networks with constant coefficients, either recursive or non-recursive, which approximate an all-pass network with 90° phase shift, or two-output digital networks which have a 90° phase difference over a wide range of frequencies. Means of implementing such phase shifting and phase splitting networks are presented.
77 citations
Posted Content•
TL;DR: In this article, the authors considered a zero mean discrete time series and defined its discrete Fourier transform at the canonical frequencies, and constructed a Portmanteau type test statistic for testing stationarity of the time series.
Abstract: We consider a zero mean discrete time series, and define its discrete Fourier transform at the canonical frequencies. It is well known that the discrete Fourier transform is asymptotically uncorrelated at the canonical frequencies if and if only the time series is second order stationary. Exploiting this important property, we construct a Portmanteau type test statistic for testing stationarity of the time series. It is shown that under the null of stationarity, the test statistic is approximately a chi square distribution. To examine the power of the test statistic, the asymptotic distribution under the locally stationary alternative is established. It is shown to be a type of noncentral chi-square, where the noncentrality parameter measures the deviation from stationarity. The test is illustrated with simulations, where is it shown to have good power. Some real examples are also included to illustrate the test.
77 citations
TL;DR: RDFT has better performance than DFT in the computation of real convolution because of the reduced number of operations, and the fact that forward and inverse transforms can be implemented with the same signal flowgraph, thereby facilitating hardware and software design.
Abstract: The real discrete Fourier transform (RDFT) corresponds to the Fourier series for sampled periodic signals with sampled periodic frequency responses just as discrete Fourier transform (DFT) corresponds to the complex Fourier series for the same type of signals RDFT has better performance than DFT in data compression and filtering for all signals in the sense that Pearl's measure for RDFT is less than Pearl's measure for DFT by an amount ΔW RDFT also has better performance than DFT in the computation of real convolution because of the reduced number of operations, and the fact that forward and inverse transforms can be implemented with the same signal flowgraph, thereby facilitating hardware and software design
77 citations
TL;DR: In this article, a modification of the differential transform method, based on the use of Pade approximants, is proposed for solving non-linear oscillatory systems, which do not exhibit periodicity.
77 citations