• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.12
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• pp.2313-2327
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• 1994

As the communication environment evolves, there is an increasing need for multiresolution image coding. To meet this need, the entrophy constratined vector quantizer(ECVQ) for coding of image pyramids by spline wavelet transform is introduced in this paper. This paper proposes a new scheme for image compression taking into account psychovisual feature both in the space and frequency domains : this proposed method involves two steps. First we use spline wavelet transform in order to obtain a set of biorthogonal subclasses of images ; the original image is decomposed at different scale using a pyramidal algorithm architecture. The decomposition is along the vertical and horizontal directions and maintains constant the number of pixels required the image. Second, according to Shannon's rate distortion theory, the wavelet coefficients are vectored quantized using a multi-resolution ECVQ(entropy-constrained vector quantizer) codebook. The simulation results showed that the proposed method could achieve higher quality LENA image improved by about 2.0 dB than that of the ECVQ using other wavelet at 0.5 bpp and, by about 0.5 dB at 1.0 bpp, and reduce the block effect and the edge degradation.

• ETRI Journal
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• v.18 no.1
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• pp.29-41
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• 1996

This paper describes a method to select a suitable feature for speech recognition using information theoretic measure. Conventional speech recognition systems heuristically choose a portion of frequency components, cepstrum, mel-cepstrum, energy, and their time differences of speech waveforms as their speech features. However, these systems never have good performance if the selected features are not suitable for speech recognition. Since the recognition rate is the only performance measure of speech recognition system, it is hard to judge how suitable the selected feature is. To solve this problem, it is essential to analyze the feature itself, and measure how good the feature itself is. Good speech features should contain all of the class-related information and as small amount of the class-irrelevant variation as possible. In this paper, we suggest a method to measure the class-related information and the amount of the class-irrelevant variation based on the Shannon's information theory. Using this method, we compare the mel-scaled FFT, cepstrum, mel-cepstrum, and wavelet features of the TIMIT speech data. The result shows that, among these features, the mel-scaled FFT is the best feature for speech recognition based on the proposed measure.

• Structural Monitoring and Maintenance
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• v.6 no.3
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• pp.219-235
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• 2019

Scheduled inspections of common crossings are one of the main cost drivers of railway maintenance. Prognostics and health management (PHM) approach and modern monitoring means offer many possibilities in the optimization of inspections and maintenance. The present paper deals with data driven prognosis of the common crossing remaining useful life (RUL) that is based on an inertial monitoring system. The problem of scheduled inspections system for common crossings is outlined and analysed. The proposed analysis of inertial signals with the maximal overlap discrete wavelet packet transform (MODWPT) and Shannon entropy (SE) estimates enable to extract the spectral features. The relevant features for the acceleration components are selected with application of Lasso (Least absolute shrinkage and selection operator) regularization. The features are fused with time domain information about the longitudinal position of wheels impact and train velocities by multivariate regression. The fused structural health (SH) indicator has a significant correlation to the lifetime of crossing. The RUL prognosis is performed on the linear degradation stochastic model with recursive Bayesian update. Prognosis testing metrics show the promising results for common crossing inspection scheduling improvement.

• Journal of Broadcast Engineering
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• v.8 no.4
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• pp.437-451
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• 2003

With the rapid growth of internet technologies and wide availability of multimedia computing facilities, the enforcement of multimedia copyright protection becomes an important issue. Digital watermarking is viewed as an effective way to deter content users from illegal distributions. In recent years, digital watermarking has been intensively studied to achieve this goal. However, when the watermarked media is transmitted over the channels modeled as the additive white Gaussian noise (AWGN) channel, the watermark information is often interfered by the channel noise and produces a large number of errors. So many error-correcting codes have been applied in the digital watermarking system to protect the embedded message from the disturbance of the noise, such as BCH codes, Reef-Solomon (RS) codes and Turbo codes. Recently, low-density parity-check (LDPC) codes were demonstrated as good error correcting codes achieving near Shannon limit performance and outperforming turbo codes nth low decoding complexity. In this paper, in order to mitigate the channel conditions and improve the quality of watermark, we proposed the application of LDPC codes on implementing a fairly robust digital image watermarking system. The implemented watermarking system operates in the spectrum domain where a subset of the discrete wavelet transform (DWT) coefficients is modified by the watermark without using original image during watermark extraction. The quality of watermark is evaluated by taking Into account the trade-off between the chip-rate and the rate of LDPC codes. Many simulation results are presented in this paper, these results indicate that the quality of the watermark is improved greatly and the proposed system based on LDPC codes is very robust to attacks.